Edge Management Service

ABSTRACT

In a particular embodiment, a storage service agent on an edge device is configured to access a particular set of storage system application programming interfaces (APIs) of at least one enterprise storage system, where the storage service agent communicatively coupled to a cloud-based storage service. The storage service agent invokes one or more storage system APIs of the particular set of storage system APIs in response to a control message from the cloud-based storage service.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation in-part application for patent entitled to afiling date and claiming the benefit of earlier-filed U.S. patentapplication Ser. No. 17/496,587, filed Oct. 7, 2021, which claimsbenefit of U.S. Provisional Applications 63/227,480, filed Jul. 30, 2021and 63/089,800, filed Oct. 9, 2020, and is a continuation in-part ofU.S. patent Ser. No. 17/157,006, filed Jan. 25, 2021, which is acontinuation in-part of U.S. Pat. No. 11,086,555, issued Aug. 10, 2021,which is a continuation of U.S. Pat. No. 10,503,427, issued Dec. 10,2019, which claims benefit of U.S. Provisional Applications 62/598,989,filed Dec. 14, 2017, 62/518,071, filed Jun. 12, 2017, 62/502,060, filedMay 5, 2017, and 62/470,172, filed Mar. 10, 2017; this application alsoclaims benefit of U.S. Provisional Patent Application No. 63/346,715,filed May 27, 2022.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 3E illustrates an example of a fleet of storage systems forproviding storage services in accordance with some embodiments of thepresent disclosure.

FIG. 4 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 5 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 6 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 7 sets forth a flow chart illustrating an example method ofestablishing a synchronous replication relationship between two or morestorage systems according to some embodiments of the present disclosure.

FIG. 8 sets forth a flow chart illustrating an additional example methodof establishing a synchronous replication relationship between two ormore storage systems according to some embodiments of the presentdisclosure.

FIG. 9 sets forth a flow chart illustrating an additional example methodof establishing a synchronous replication relationship between two ormore storage systems according to some embodiments of the presentdisclosure.

FIG. 10 sets forth a flow chart illustrating an additional examplemethod of application replication among storage systems synchronouslyreplicating a dataset according to some embodiments of the presentdisclosure.

FIG. 11 sets forth a flow chart illustrating an example method forapplication replication among storage systems synchronously replicatinga dataset according to some embodiments of the present disclosure.

FIG. 12 sets forth a flow chart illustrating an additional examplemethod for application replication among storage systems synchronouslyreplicating a dataset according to some embodiments of the presentdisclosure.

FIG. 13 sets forth a flow chart illustrating an additional examplemethod for application replication among storage systems synchronouslyreplicating a dataset according to some embodiments of the presentdisclosure.

FIG. 14 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 15 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 16 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 17 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 18 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 19 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 20 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 21 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 22 sets forth a flow chart illustrating an additional examplemethod for data path virtualization according to some embodiments of thepresent disclosure.

FIG. 23 sets forth a diagram of an example edge management servicearchitecture according to some embodiments of the present disclosure.

FIG. 24 sets forth a flow chart illustrating an additional examplemethod of employing an edge management service according to someembodiments of the present disclosure.

FIG. 25 sets forth a flow chart illustrating an additional examplemethod of employing an edge management service according to someembodiments of the present disclosure.

FIG. 26 sets forth a flow chart illustrating an additional examplemethod of employing an edge management service according to someembodiments of the present disclosure.

FIG. 27 sets forth a flow chart illustrating an additional examplemethod of employing an edge management service according to someembodiments of the present disclosure.

FIG. 28 sets forth a flow chart illustrating an additional examplemethod of employing an edge management service according to someembodiments of the present disclosure.

FIG. 29 sets forth a flow chart illustrating an additional examplemethod of employing an edge management service according to someembodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for an edge management servicein accordance with embodiments of the present disclosure are describedwith reference to the accompanying drawings, beginning with FIG. 1A.FIG. 1A illustrates an example system for data storage, in accordancewith some implementations. System 100 (also referred to as “storagesystem” herein) includes numerous elements for purposes of illustrationrather than limitation. It may be noted that system 100 may include thesame, more, or fewer elements configured in the same or different mannerin other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAIVIdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAIVI device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In implementations utilizing an SSD, thenatural size may be based on the erase block size of the SSD. In someimplementations, the zones of the zoned storage device may be definedduring initialization of the zoned storage device. In implementations,the zones may be defined dynamically as data is written to the zonedstorage device.

In some implementations, zones may be heterogeneous, with some zoneseach being a page group and other zones being multiple page groups. Inimplementations, some zones may correspond to an erase block and otherzones may correspond to multiple erase blocks. In an implementation,zones may be any combination of differing numbers of pages in pagegroups and/or erase blocks, for heterogeneous mixes of programmingmodes, manufacturers, product types and/or product generations ofstorage devices, as applied to heterogeneous assemblies, upgrades,distributed storages, etc. In some implementations, zones may be definedas having usage characteristics, such as a property of supporting datawith particular kinds of longevity (very short lived or very long lived,for example). These properties could be used by a zoned storage deviceto determine how the zone will be managed over the zone's expectedlifetime.

It should be appreciated that a zone is a virtual construct. Anyparticular zone may not have a fixed location at a storage device. Untilallocated, a zone may not have any location at a storage device. A zonemay correspond to a number representing a chunk of virtually allocatablespace that is the size of an erase block or other block size in variousimplementations. When the system allocates or opens a zone, zones getallocated to flash or other solid-state storage memory and, as thesystem writes to the zone, pages are written to that mapped flash orother solid-state storage memory of the zoned storage device. When thesystem closes the zone, the associated erase block(s) or other sizedblock(s) are completed. At some point in the future, the system maydelete a zone which will free up the zone's allocated space. During itslifetime, a zone may be moved around to different locations of the zonedstorage device, e.g., as the zoned storage device does internalmaintenance.

In implementations, the zones of the zoned storage device may be indifferent states. A zone may be in an empty state in which data has notbeen stored at the zone. An empty zone may be opened explicitly, orimplicitly by writing data to the zone. This is the initial state forzones on a fresh zoned storage device, but may also be the result of azone reset. In some implementations, an empty zone may have a designatedlocation within the flash memory of the zoned storage device. In animplementation, the location of the empty zone may be chosen when thezone is first opened or first written to (or later if writes arebuffered into memory). A zone may be in an open state either implicitlyor explicitly, where a zone that is in an open state may be written tostore data with write or append commands. In an implementation, a zonethat is in an open state may also be written to using a copy commandthat copies data from a different zone. In some implementations, a zonedstorage device may have a limit on the number of open zones at aparticular time.

A zone in a closed state is a zone that has been partially written to,but has entered a closed state after issuing an explicit closeoperation. A zone in a closed state may be left available for futurewrites, but may reduce some of the run-time overhead consumed by keepingthe zone in an open state. In implementations, a zoned storage devicemay have a limit on the number of closed zones at a particular time. Azone in a full state is a zone that is storing data and can no longer bewritten to. A zone may be in a full state either after writes havewritten data to the entirety of the zone or as a result of a zone finishoperation. Prior to a finish operation, a zone may or may not have beencompletely written. After a finish operation, however, the zone may notbe opened a written to further without first performing a zone resetoperation.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In implementations utilizing an HDD, the resetting of the zone may allowthe shingle tracks to be allocated to a new, opened zone that may beopened at some point in the future. In implementations utilizing an SSD,the resetting of the zone may cause the associated physical eraseblock(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storage devicecontroller 119. In one embodiment, storage device controller 119A-D maybe a CPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the stored energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example storage system 124 for data storagein accordance with some implementations. In one embodiment, storagesystem 124 includes storage controllers 125 a, 125 b. In one embodiment,storage controllers 125 a, 125 b are operatively coupled to Dual PCIstorage devices. Storage controllers 125 a, 125 b may be operativelycoupled (e.g., via a storage network 130) to some number of hostcomputers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, storage controllers 125 a, 125 b operate as PCImasters to one or the other PCI buses 128 a, 128 b. In anotherembodiment, 128 a and 128 b may be based on other communicationsstandards (e.g., HyperTransport, InfiniBand, etc.). Other storage systemembodiments may operate storage controllers 125 a, 125 b asmulti-masters for both PCI buses 128 a, 128 b. Alternately, aPCl/NVMe/NVMf switching infrastructure or fabric may connect multiplestorage controllers. Some storage system embodiments may allow storagedevices to communicate with each other directly rather thancommunicating only with storage controllers. In one embodiment, astorage device controller 119 a may be operable under direction from astorage controller 125 a to synthesize and transfer data to be storedinto Flash memory devices from data that has been stored in RAM (e.g.,RAM 121 of FIG. 1C). For example, a recalculated version of RAM contentmay be transferred after a storage controller has determined that anoperation has fully committed across the storage system, or whenfast-write memory on the device has reached a certain used capacity, orafter a certain amount of time, to ensure improve safety of the data orto release addressable fast-write capacity for reuse. This mechanism maybe used, for example, to avoid a second transfer over a bus (e.g., 128a, 128 b) from the storage controllers 125 a, 125 b. In one embodiment,a recalculation may include compressing data, attaching indexing orother metadata, combining multiple data segments together, performingerasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one storage controller 125 a to another storage controller 125b, or it could be used to offload compression, data aggregation, and/orerasure coding calculations and transfers to storage devices to reduceload on storage controllers or the storage controller interface 129 a,129 b to the PCI bus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storage152 units or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storage 152, for exampleas lists or other data structures stored in memory. In some embodimentsthe authorities are stored within the non-volatile solid state storage152 and supported by software executing on a controller or otherprocessor of the non-volatile solid state storage 152. In a furtherembodiment, authorities 168 are implemented on the storage nodes 150,for example as lists or other data structures stored in the memory 154and supported by software executing on the CPU 156 of the storage node150. Authorities 168 control how and where data is stored in thenon-volatile solid state storage 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storage 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIG. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage 152 unit may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The non-volatile solid statestorage 152 units described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 161, as described herein, multiple controllers inmultiple non-volatile sold state storage 152 units and/or storage nodes150 cooperate in various ways (e.g., for erasure coding, data sharding,metadata communication and redundancy, storage capacity expansion orcontraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage 152 units of FIGS. 2A-C. In thisversion, each non-volatile solid state storage 152 unit has a processorsuch as controller 212 (see FIG. 2C), an FPGA, flash memory 206, andNVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and2C) on a PCIe (peripheral component interconnect express) board in achassis 138 (see FIG. 2A). The non-volatile solid state storage 152 unitmay be implemented as a single board containing storage, and may be thelargest tolerable failure domain inside the chassis. In someembodiments, up to two non-volatile solid state storage 152 units mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the non-volatile solid state storage 152 DRAM 216,and is backed by NAND flash. NVRAM 204 is logically rdivided intomultiple memory regions written for two as spool (e.g., spool_region).Space within the NVRAM 204 spools is managed by each authority 168independently. Each device provides an amount of storage space to eachauthority 168. That authority 168 further manages lifetimes andallocations within that space. Examples of a spool include distributedtransactions or notions. When the primary power to a non-volatile solidstate storage 152 unit fails, onboard super-capacitors provide a shortduration of power hold up. During this holdup interval, the contents ofthe NVRAM 204 are flushed to flash memory 206. On the next power-on, thecontents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage system 306 and remote, cloud-based storage thatis utilized by the storage system 306. Through the use of a cloudstorage gateway, organizations may move primary iSCSI or NAS to thecloud services provider 302, thereby enabling the organization to savespace on their on-premises storage systems. Such a cloud storage gatewaymay be configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache, storage resources within the storage system may beutilized as a read cache, or tiering may be achieved within the storagesystems by placing data within the storage system in accordance with oneor more tiering policies.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘i SC SI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques. Such data protection techniques may be carriedout, for example, by system software executing on computer hardwarewithin the storage system, by a cloud services provider, or in otherways. Such data protection techniques can include data archiving, databackup, data replication, data snapshotting, data and database cloning,and other data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage system 306. For example, the software resources 314 may includesoftware modules that perform various data reduction techniques such as,for example, data compression, data deduplication, and others. Thesoftware resources 314 may include software modules that intelligentlygroup together I/O operations to facilitate better usage of theunderlying storage resource 308, software modules that perform datamigration operations to migrate from within a storage system, as well assoftware modules that perform other functions. Such software resources314 may be embodied as one or more software containers or in many otherways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’)™, Microsoft Azure™, GoogleCloud Platform™, IBM Cloud™, Oracle Cloud™, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. For example, each of the cloud computing instances 320, 322 mayexecute on an Azure VM, where each Azure VM may include high speedtemporary storage that may be leveraged as a cache (e.g., as a readcache). In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data to the cloud-based storage system 318,erasing data from the cloud-based storage system 318, retrieving datafrom the cloud-based storage system 318, monitoring and reporting ofdisk utilization and performance, performing redundancy operations, suchas RAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322 such as distinct EC2instances.

Readers will appreciate that other embodiments that do not include aprimary and secondary controller are within the scope of the presentdisclosure. For example, each cloud computing instance 320, 322 mayoperate as a primary controller for some portion of the address spacesupported by the cloud-based storage system 318, each cloud computinginstance 320, 322 may operate as a primary controller where theservicing of I/O operations directed to the cloud-based storage system318 are divided in some other way, and so on. In fact, in otherembodiments where costs savings may be prioritized over performancedemands, only a single cloud computing instance may exist that containsthe storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n may be embodied,for example, as instances of cloud computing resources that may beprovided by the cloud computing environment 316 to support the executionof software applications. The cloud computing instances 340 a, 340 b,340 n of FIG. 3C may differ from the cloud computing instances 320, 322described above as the cloud computing instances 340 a, 340 b, 340 n ofFIG. 3C have local storage 330, 334, 338 resources whereas the cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 need not have local storage resources.The cloud computing instances 340 a, 340 b, 340 n with local storage330, 334, 338 may be embodied, for example, as EC2 M5 instances thatinclude one or more SSDs, as EC2 R5 instances that include one or moreSSDs, as EC2 I3 instances that include one or more SSDs, and so on. Insome embodiments, the local storage 330, 334, 338 must be embodied assolid-state storage (e.g., SSDs) rather than storage that makes use ofhard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block storage 342, 344, 346 that is offered by the cloudcomputing environment 316 such as, for example, as Amazon Elastic BlockStore (‘EBS’) volumes. In such an example, the block storage 342, 344,346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM. In yet another embodiment, high performanceblock storage resources such as one or more Azure Ultra Disks may beutilized as the NVRAM.

The storage controller applications 324, 326 may be used to performvarious tasks such as deduplicating the data contained in the request,compressing the data contained in the request, determining where to thewrite the data contained in the request, and so on, before ultimatelysending a request to write a deduplicated, encrypted, or otherwisepossibly updated version of the data to one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. Either cloud computing instance 320, 322, in some embodiments, mayreceive a request to read data from the cloud-based storage system 318and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

When a request to write data is received by a particular cloud computinginstance 340 a, 340 b, 340 n with local storage 330, 334, 338, thesoftware daemon 328, 332, 336 may be configured to not only write thedata to its own local storage 330, 334, 338 resources and anyappropriate block storage 342, 344, 346 resources, but the softwaredaemon 328, 332, 336 may also be configured to write the data tocloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n. The cloud-based object storage348 that is attached to the particular cloud computing instance 340 a,340 b, 340 n may be embodied, for example, as Amazon Simple StorageService (‘S3’). In other embodiments, the cloud computing instances 320,322 that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348. In other embodiments, rather than using both thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 (also referred to herein as ‘virtual drives’) and thecloud-based object storage 348 to store data, a persistent storage layermay be implemented in other ways. For example, one or more Azure Ultradisks may be used to persistently store data (e.g., after the data hasbeen written to the NVRAM layer).

While the local storage 330, 334, 338 resources and the block storage342, 344, 346 resources that are utilized by the cloud computinginstances 340 a, 340 b, 340 n may support block-level access, thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n supports only object-basedaccess. The software daemon 328, 332, 336 may therefore be configured totake blocks of data, package those blocks into objects, and write theobjects to the cloud-based object storage 348 that is attached to theparticular cloud computing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 may also beconfigured to create five objects containing distinct 1 MB chunks of thedata. As such, in some embodiments, each object that is written to thecloud-based object storage 348 may be identical (or nearly identical) insize. Readers will appreciate that in such an example, metadata that isassociated with the data itself may be included in each object (e.g.,the first 1 MB of the object is data and the remaining portion ismetadata associated with the data). Readers will appreciate that thecloud-based object storage 348 may be incorporated into the cloud-basedstorage system 318 to increase the durability of the cloud-based storagesystem 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

One or more modules of computer program instructions that are executingwithin the cloud-based storage system 318 (e.g., a monitoring modulethat is executing on its own EC2 instance) may be designed to handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338. In such an example, themonitoring module may handle the failure of one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334, 338by creating one or more new cloud computing instances with localstorage, retrieving data that was stored on the failed cloud computinginstances 340 a, 340 b, 340 n from the cloud-based object storage 348,and storing the data retrieved from the cloud-based object storage 348in local storage on the newly created cloud computing instances. Readerswill appreciate that many variants of this process may be implemented.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Forexample, if the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318, a monitoringmodule may create a new, more powerful cloud computing instance (e.g., acloud computing instance of a type that includes more processing power,more memory, etc. . . . ) that includes the storage controllerapplication such that the new, more powerful cloud computing instancecan begin operating as the primary controller. Likewise, if themonitoring module determines that the cloud computing instances 320, 322that are used to support the execution of a storage controllerapplication 324, 326 are oversized and that cost savings could be gainedby switching to a smaller, less powerful cloud computing instance, themonitoring module may create a new, less powerful (and less expensive)cloud computing instance that includes the storage controllerapplication such that the new, less powerful cloud computing instancecan begin operating as the primary controller.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described in thisdisclosure may be useful for supporting various types of softwareapplications. In fact, the storage systems may be ‘application aware’ inthe sense that the storage systems may obtain, maintain, or otherwisehave access to information describing connected applications (e.g.,applications that utilize the storage systems) to optimize the operationof the storage system based on intelligence about the applications andtheir utilization patterns. For example, the storage system may optimizedata layouts, optimize caching behaviors, optimize ‘QoS’ levels, orperform some other optimization that is designed to improve the storageperformance that is experienced by the application.

As an example of one type of application that may be supported by thestorage systems describe herein, the storage system 306 may be useful insupporting artificial intelligence (‘AI’) applications, databaseapplications, XOps projects (e.g., DevOps projects, DataOps projects,MLOps projects, ModelOps projects, PlatformOps projects), electronicdesign automation tools, event-driven software applications, highperformance computing applications, simulation applications, high-speeddata capture and analysis applications, machine learning applications,media production applications, media serving applications, picturearchiving and communication systems (‘PACS’) applications, softwaredevelopment applications, virtual reality applications, augmentedreality applications, and many other types of applications by providingstorage resources to such applications.

In view of the fact that the storage systems include compute resources,storage resources, and a wide variety of other resources, the storagesystems may be well suited to support applications that are resourceintensive such as, for example, AI applications. AI applications may bedeployed in a variety of fields, including: predictive maintenance inmanufacturing and related fields, healthcare applications such aspatient data & risk analytics, retail and marketing deployments (e.g.,search advertising, social media advertising), supply chains solutions,fintech solutions such as business analytics & reporting tools,operational deployments such as real-time analytics tools, applicationperformance management tools, IT infrastructure management tools, andmany others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson™, MicrosoftOxford™, Google DeepMind™, Baidu Minwa™, and others.

The storage systems described above may also be well suited to supportother types of applications that are resource intensive such as, forexample, machine learning applications. Machine learning applicationsmay perform various types of data analysis to automate analytical modelbuilding. Using algorithms that iteratively learn from data, machinelearning applications can enable computers to learn without beingexplicitly programmed. One particular area of machine learning isreferred to as reinforcement learning, which involves taking suitableactions to maximize reward in a particular situation.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia GPUs and the storage systemsmay support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains and derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics, including being leveraged as part of a composable dataanalytics pipeline where containerized analytics architectures, forexample, make analytics capabilities more composable. Big data analyticsmay be generally described as the process of examining large and varieddata sets to uncover hidden patterns, unknown correlations, markettrends, customer preferences and other useful information that can helporganizations make more-informed business decisions. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa™, Apple Siri™, Google Voice™, Samsung Bixby™,Microsoft Cortana™, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

The storage systems described herein may be used to form a data lake. Adata lake may operate as the first place that an organization's dataflows to, where such data may be in a raw format. Metadata tagging maybe implemented to facilitate searches of data elements in the data lake,especially in embodiments where the data lake contains multiple storesof data, in formats not easily accessible or readable (e.g.,unstructured data, semi-structured data, structured data). From the datalake, data may go downstream to a data warehouse where data may bestored in a more processed, packaged, and consumable format. The storagesystems described above may also be used to implement such a datawarehouse. In addition, a data mart or data hub may allow for data thatis even more easily consumed, where the storage systems described abovemay also be used to provide the underlying storage resources necessaryfor a data mart or data hub. In embodiments, queries the data lake mayrequire a schema-on-read approach, where data is applied to a plan orschema as it is pulled out of a stored location, rather than as it goesinto the stored location.

The storage systems described herein may also be configured implement arecovery point objective (‘RPO’), which may be establish by a user,established by an administrator, established as a system default,established as part of a storage class or service that the storagesystem is participating in the delivery of, or in some other way. A“recovery point objective” is a goal for the maximum time differencebetween the last update to a source dataset and the last recoverablereplicated dataset update that would be correctly recoverable, given areason to do so, from a continuously or frequently updated copy of thesource dataset. An update is correctly recoverable if it properly takesinto account all updates that were processed on the source dataset priorto the last recoverable replicated dataset update.

In synchronous replication, the RPO would be zero, meaning that undernormal operation, all completed updates on the source dataset should bepresent and correctly recoverable on the copy dataset. In best effortnearly synchronous replication, the RPO can be as low as a few seconds.In snapshot-based replication, the RPO can be roughly calculated as theinterval between snapshots plus the time to transfer the modificationsbetween a previous already transferred snapshot and the most recentto-be-replicated snapshot.

If updates accumulate faster than they are replicated, then an RPO canbe missed. If more data to be replicated accumulates between twosnapshots, for snapshot-based replication, than can be replicatedbetween taking the snapshot and replicating that snapshot's cumulativeupdates to the copy, then the RPO can be missed. If, again insnapshot-based replication, data to be replicated accumulates at afaster rate than could be transferred in the time between subsequentsnapshots, then replication can start to fall further behind which canextend the miss between the expected recovery point objective and theactual recovery point that is represented by the last correctlyreplicated update.

The storage systems described above may also be part of a shared nothingstorage cluster. In a shared nothing storage cluster, each node of thecluster has local storage and communicates with other nodes in thecluster through networks, where the storage used by the cluster is (ingeneral) provided only by the storage connected to each individual node.A collection of nodes that are synchronously replicating a dataset maybe one example of a shared nothing storage cluster, as each storagesystem has local storage and communicates to other storage systemsthrough a network, where those storage systems do not (in general) usestorage from somewhere else that they share access to through some kindof interconnect. In contrast, some of the storage systems describedabove are themselves built as a shared-storage cluster, since there aredrive shelves that are shared by the paired controllers. Other storagesystems described above, however, are built as a shared nothing storagecluster, as all storage is local to a particular node (e.g., a blade)and all communication is through networks that link the compute nodestogether.

In other embodiments, other forms of a shared nothing storage clustercan include embodiments where any node in the cluster has a local copyof all storage they need, and where data is mirrored through asynchronous style of replication to other nodes in the cluster either toensure that the data isn't lost or because other nodes are also usingthat storage. In such an embodiment, if a new cluster node needs somedata, that data can be copied to the new node from other nodes that havecopies of the data.

In some embodiments, mirror-copy-based shared storage clusters may storemultiple copies of all the cluster's stored data, with each subset ofdata replicated to a particular set of nodes, and different subsets ofdata replicated to different sets of nodes. In some variations,embodiments may store all of the cluster's stored data in all nodes,whereas in other variations nodes may be divided up such that a firstset of nodes will all store the same set of data and a second, differentset of nodes will all store a different set of data.

Readers will appreciate that RAFT-based databases (e.g., etcd) mayoperate like shared-nothing storage clusters where all RAFT nodes storeall data. The amount of data stored in a RAFT cluster, however, may belimited so that extra copies don't consume too much storage. A containerserver cluster might also be able to replicate all data to all clusternodes, presuming the containers don't tend to be too large and theirbulk data (the data manipulated by the applications that run in thecontainers) is stored elsewhere such as in an S3 cluster or an externalfile server. In such an example, the container storage may be providedby the cluster directly through its shared-nothing storage model, withthose containers providing the images that form the executionenvironment for parts of an application or service.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 3E illustrates an example of a fleet ofstorage systems 376 for providing storage services (also referred toherein as ‘data services’). The fleet of storage systems 376 depicted inFIG. 3 includes a plurality of storage systems 374 a, 374 b, 374 c, 374d, 374 n that may each be similar to the storage systems describedherein. The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in thefleet of storage systems 376 may be embodied as identical storagesystems or as different types of storage systems. For example, two ofthe storage systems 374 a, 374 n depicted in FIG. 3E are depicted asbeing cloud-based storage systems, as the resources that collectivelyform each of the storage systems 374 a, 374 n are provided by distinctcloud services providers 370, 372. For example, the first cloud servicesprovider 370 may be Amazon AWS™ whereas the second cloud servicesprovider 372 is Microsoft Azure™, although in other embodiments one ormore public clouds, private clouds, or combinations thereof may be usedto provide the underlying resources that are used to form a particularstorage system in the fleet of storage systems 376.

The example depicted in FIG. 3E includes an edge management service 382for delivering storage services in accordance with some embodiments ofthe present disclosure. The storage services (also referred to herein as‘data services’) that are delivered may include, for example, servicesto provide a certain amount of storage to a consumer, services toprovide storage to a consumer in accordance with a predetermined servicelevel agreement, services to provide storage to a consumer in accordancewith predetermined regulatory requirements, and many others.

The edge management service 382 depicted in FIG. 3E may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware such as one or more computer processors.Alternatively, the edge management service 382 may be embodied as one ormore modules of computer program instructions executing on a virtualizedexecution environment such as one or more virtual machines, in one ormore containers, or in some other way. In other embodiments, the edgemanagement service 382 may be embodied as a combination of theembodiments described above, including embodiments where the one or moremodules of computer program instructions that are included in the edgemanagement service 382 are distributed across multiple physical orvirtual execution environments.

The edge management service 382 may operate as a gateway for providingstorage services to storage consumers, where the storage servicesleverage storage offered by one or more storage systems 374 a, 374 b,374 c, 374 d, 374 n. For example, the edge management service 382 may beconfigured to provide storage services to host devices 378 a, 378 b, 378c, 378 d, 378 n that are executing one or more applications that consumethe storage services. In such an example, the edge management service382 may operate as a gateway between the host devices 378 a, 378 b, 378c, 378 d, 378 n and the storage systems 374 a, 374 b, 374 c, 374 d, 374n, rather than requiring that the host devices 378 a, 378 b, 378 c, 378d, 378 n directly access the storage systems 374 a, 374 b, 374 c, 374 d,374 n.

The edge management service 382 of FIG. 3E exposes a storage servicesmodule 380 to the host devices 378 a, 378 b, 378 c, 378 d, 378 n of FIG.3E, although in other embodiments the edge management service 382 mayexpose the storage services module 380 to other consumers of the variousstorage services. The various storage services may be presented toconsumers via one or more user interfaces, via one or more APIs, orthrough some other mechanism provided by the storage services module380. As such, the storage services module 380 depicted in FIG. 3E may beembodied as one or more modules of computer program instructionsexecuting on physical hardware, on a virtualized execution environment,or combinations thereof, where executing such modules causes enables aconsumer of storage services to be offered, select, and access thevarious storage services.

The edge management service 382 of FIG. 3E also includes a systemmanagement services module 384. The system management services module384 of FIG. 3E includes one or more modules of computer programinstructions that, when executed, perform various operations incoordination with the storage systems 374 a, 374 b, 374 c, 374 d, 374 nto provide storage services to the host devices 378 a, 378 b, 378 c, 378d, 378 n. The system management services module 384 may be configured,for example, to perform tasks such as provisioning storage resourcesfrom the storage systems 374 a, 374 b, 374 c, 374 d, 374 n via one ormore APIs exposed by the storage systems 374 a, 374 b, 374 c, 374 d, 374n, migrating datasets or workloads amongst the storage systems 374 a,374 b, 374 c, 374 d, 374 n via one or more APIs exposed by the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n, setting one or more tunableparameters (i.e., one or more configurable settings) on the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n via one or more APIs exposedby the storage systems 374 a, 374 b, 374 c, 374 d, 374 n, and so on. Forexample, many of the services described below relate to embodimentswhere the storage systems 374 a, 374 b, 374 c, 374 d, 374 n areconfigured to operate in some way. In such examples, the systemmanagement services module 384 may be responsible for using APIs (orsome other mechanism) provided by the storage systems 374 a, 374 b, 374c, 374 d, 374 n to configure the storage systems 374 a, 374 b, 374 c,374 d, 374 n to operate in the ways described below.

In addition to configuring the storage systems 374 a, 374 b, 374 c, 374d, 374 n, the edge management service 382 itself may be configured toperform various tasks required to provide the various storage services.Consider an example in which the storage service includes a servicethat, when selected and applied, causes personally identifiableinformation (PIP) contained in a dataset to be obfuscated when thedataset is accessed. In such an example, the storage systems 374 a, 374b, 374 c, 374 d, 374 n may be configured to obfuscate PII when servicingread requests directed to the dataset. Alternatively, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may service reads by returningdata that includes the PII, but the edge management service 382 itselfmay obfuscate the PII as the data is passed through the edge managementservice 382 on its way from the storage systems 374 a, 374 b, 374 c, 374d, 374 n to the host devices 378 a, 378 b, 378 c, 378 d, 378 n.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may be embodied as one or more of the storage systems described abovewith reference to FIGS. 1A-3D, including variations thereof. In fact,the storage systems 374 a, 374 b, 374 c, 374 d, 374 n may serve as apool of storage resources where the individual components in that poolhave different performance characteristics, different storagecharacteristics, and so on. For example, one of the storage systems 374a may be a cloud-based storage system, another storage system 374 b maybe a storage system that provides block storage, another storage system374 c may be a storage system that provides file storage, anotherstorage system 374 d may be a relatively high-performance storage systemwhile another storage system 374 n may be a relatively low-performancestorage system, and so on. In alternative embodiments, only a singlestorage system may be present.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may also be organized into different failure domains so that thefailure of one storage system 374 a should be totally unrelated to thefailure of another storage system 374 b. For example, each of thestorage systems may receive power from independent power systems, eachof the storage systems may be coupled for data communications overindependent data communications networks, and so on. Furthermore, thestorage systems in a first failure domain may be accessed via a firstgateway whereas storage systems in a second failure domain may beaccessed via a second gateway. For example, the first gateway may be afirst instance of the edge management service 382 and the second gatewaymay be a second instance of the edge management service 382, includingembodiments where each instance is distinct, or each instance is part ofa distributed edge management service 382.

As an illustrative example of available storage services, storageservices may be presented to a user that are associated with differentlevels of data protection. For example, storage services may bepresented to the user that, when selected and enforced, guarantee theuser that data associated with that user will be protected such thatvarious recovery point objectives (‘RPO’) can be guaranteed. A firstavailable storage service may ensure, for example, that some datasetassociated with the user will be protected such that any data that ismore than 5 seconds old can be recovered in the event of a failure ofthe primary data store whereas a second available storage service mayensure that the dataset that is associated with the user will beprotected such that any data that is more than 5 minutes old can berecovered in the event of a failure of the primary data store.

An additional example of storage services that may be presented to auser, selected by a user, and ultimately applied to a dataset associatedwith the user can include one or more data compliance services. Suchdata compliance services may be embodied, for example, as services thatmay be provided to consumers (i.e., a user) the data compliance servicesto ensure that the user's datasets are managed in a way to adhere tovarious regulatory requirements. For example, one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the General DataProtection Regulation (‘GDPR’), one or data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to the Sarbanes-Oxley Act of 2002 (‘SOX’), or one ormore data compliance services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to adhere to some otherregulatory act. In addition, the one or more data compliance servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to adhere to some non-governmental guidance (e.g., toadhere to best practices for auditing purposes), the one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to a particular clients ororganizations requirements, and so on.

Consider an example in which a particular data compliance service isdesigned to ensure that a user's datasets are managed in a way so as toadhere to the requirements set forth in the GDPR. While a listing of allrequirements of the GDPR can be found in the regulation itself, for thepurposes of illustration, an example requirement set forth in the GDPRrequires that pseudonymization processes must be applied to stored datain order to transform personal data in such a way that the resultingdata cannot be attributed to a specific data subject without the use ofadditional information. For example, data encryption techniques can beapplied to render the original data unintelligible, and such dataencryption techniques cannot be reversed without access to the correctdecryption key. As such, the GDPR may require that the decryption key bekept separately from the pseudonymised data. One particular datacompliance service may be offered to ensure adherence to therequirements set forth in this paragraph.

In order to provide this particular data compliance service, the datacompliance service may be presented to a user (e.g., via a GUI) andselected by the user. In response to receiving the selection of theparticular data compliance service, one or more storage servicespolicies may be applied to a dataset associated with the user to carryout the particular data compliance service. For example, a storageservices policy may be applied requiring that the dataset be encryptedprior to be stored in a storage system, prior to being stored in a cloudenvironment, or prior to being stored elsewhere. In order to enforcethis policy, a requirement may be enforced not only requiring that thedataset be encrypted when stored, but a requirement may be put in placerequiring that the dataset be encrypted prior to transmitting thedataset (e.g., sending the dataset to another party). In such anexample, a storage services policy may also be put in place requiringthat any encryption keys used to encrypt the dataset are not stored onthe same system that stores the dataset itself. Readers will appreciatethat many other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in the fleet ofstorage systems 376 may be managed collectively, for example, by one ormore fleet management modules. The fleet management modules may be partof or separate from the system management services module 384 depictedin FIG. 3E. The fleet management modules may perform tasks such asmonitoring the health of each storage system in the fleet, initiatingupdates or upgrades on one or more storage systems in the fleet,migrating workloads for loading balancing or other performance purposes,and many other tasks. As such, and for many other reasons, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may be coupled to each othervia one or more data communications links in order to exchange databetween the storage systems 374 a, 374 b, 374 c, 374 d, 374 n.

The storage systems described herein may support various forms of datareplication. For example, two or more of the storage systems maysynchronously replicate a dataset between each other. In synchronousreplication, distinct copies of a particular dataset may be maintainedby multiple storage systems, but all accesses (e.g., a read) of thedataset should yield consistent results regardless of which storagesystem the access was directed to. For example, a read directed to anyof the storage systems that are synchronously replicating the datasetshould return identical results. As such, while updates to the versionof the dataset need not occur at exactly the same time, precautions mustbe taken to ensure consistent accesses to the dataset. For example, ifan update (e.g., a write) that is directed to the dataset is received bya first storage system, the update may only be acknowledged as beingcompleted if all storage systems that are synchronously replicating thedataset have applied the update to their copies of the dataset. In suchan example, synchronous replication may be carried out through the useof I/O forwarding (e.g., a write received at a first storage system isforwarded to a second storage system), communications between thestorage systems (e.g., each storage system indicating that it hascompleted the update), or in other ways.

In other embodiments, a dataset may be replicated through the use ofcheckpoints. In checkpoint-based replication (also referred to as‘nearly synchronous replication’), a set of updates to a dataset (e.g.,one or more write operations directed to the dataset) may occur betweendifferent checkpoints, such that a dataset has been updated to aspecific checkpoint only if all updates to the dataset prior to thespecific checkpoint have been completed. Consider an example in which afirst storage system stores a live copy of a dataset that is beingaccessed by users of the dataset. In this example, assume that thedataset is being replicated from the first storage system to a secondstorage system using checkpoint-based replication. For example, thefirst storage system may send a first checkpoint (at time t=0) to thesecond storage system, followed by a first set of updates to thedataset, followed by a second checkpoint (at time t=1), followed by asecond set of updates to the dataset, followed by a third checkpoint (attime t=2). In such an example, if the second storage system hasperformed all updates in the first set of updates but has not yetperformed all updates in the second set of updates, the copy of thedataset that is stored on the second storage system may be up-to-dateuntil the second checkpoint. Alternatively, if the second storage systemhas performed all updates in both the first set of updates and thesecond set of updates, the copy of the dataset that is stored on thesecond storage system may be up-to-date until the third checkpoint.Readers will appreciate that various types of checkpoints may be used(e.g., metadata only checkpoints), checkpoints may be spread out basedon a variety of factors (e.g., time, number of operations, an RPOsetting), and so on.

In other embodiments, a dataset may be replicated through snapshot-basedreplication (also referred to as ‘asynchronous replication’). Insnapshot-based replication, snapshots of a dataset may be sent from areplication source such as a first storage system to a replicationtarget such as a second storage system. In such an embodiment, eachsnapshot may include the entire dataset or a subset of the dataset suchas, for example, only the portions of the dataset that have changedsince the last snapshot was sent from the replication source to thereplication target. Readers will appreciate that snapshots may be senton-demand, based on a policy that takes a variety of factors intoconsideration (e.g., time, number of operations, an RPO setting), or insome other way.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g., a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

For further explanation, FIG. 4 sets forth a block diagram illustratinga plurality of storage systems (402, 404, 406) that support a podaccording to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage systems (402, 404, 406) depicted inFIG. 4 may be similar to the storage systems described above withreference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, or any combinationthereof. In fact, the storage systems (402, 404, 406) depicted in FIG. 4may include the same, fewer, or additional components as the storagesystems described above.

In the example depicted in FIG. 4, each of the storage systems (402,404, 406) is depicted as having at least one computer processor (408,410, 412), computer memory (414, 416, 418), and computer storage (420,422, 424). Although in some embodiments the computer memory (414, 416,418) and the computer storage (420, 422, 424) may be part of the samehardware devices, in other embodiments the computer memory (414, 416,418) and the computer storage (420, 422, 424) may be part of differenthardware devices. The distinction between the computer memory (414, 416,418) and the computer storage (420, 422, 424) in this particular examplemay be that the computer memory (414, 416, 418) is physically proximateto the computer processors (408, 410, 412) and may store computerprogram instructions that are executed by the computer processors (408,410, 412), while the computer storage (420, 422, 424) is embodied asnon-volatile storage for storing user data, metadata describing the userdata, and so on. Referring to the example above in FIG. 1A, for example,the computer processors (408, 410, 412) and computer memory (414, 416,418) for a particular storage system (402, 404, 406) may reside withinone of more of the controllers (110A-110D) while the attached storagedevices (171A-171F) may serve as the computer storage (420, 422, 424)within a particular storage system (402, 404, 406).

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may attach to one or more pods (430, 432) according to someembodiments of the present disclosure. Each of the pods (430, 432)depicted in FIG. 4 can include a dataset (426, 428). For example, afirst pod (430) that three storage systems (402, 404, 406) have attachedto includes a first dataset (426) while a second pod (432) that twostorage systems (404, 406) have attached to includes a second dataset(428). In such an example, when a particular storage system attaches toa pod, the pod's dataset is copied to the particular storage system andthen kept up to date as the dataset is modified. Storage systems can beremoved from a pod, resulting in the dataset being no longer kept up todate on the removed storage system. In the example depicted in FIG. 4,any storage system which is active for a pod (it is an up-to-date,operating, non-faulted member of a non-faulted pod) can receive andprocess requests to modify or read the pod's dataset.

In the example depicted in FIG. 4, each pod (430, 432) may also includea set of managed objects and management operations, as well as a set ofaccess operations to modify or read the dataset (426, 428) that isassociated with the particular pod (430, 432). In such an example, themanagement operations may modify or query managed objects equivalentlythrough any of the storage systems. Likewise, access operations to reador modify the dataset may operate equivalently through any of thestorage systems. In such an example, while each storage system stores aseparate copy of the dataset as a proper subset of the datasets storedand advertised for use by the storage system, the operations to modifymanaged objects or the dataset performed and completed through any onestorage system are reflected in subsequent management objects to querythe pod or subsequent access operations to read the dataset.

Readers will appreciate that pods may implement more capabilities thanjust a clustered synchronously replicated dataset. For example, pods canbe used to implement tenants, whereby datasets are in some way securelyisolated from each other. Pods can also be used to implement virtualarrays or virtual storage systems where each pod is presented as aunique storage entity on a network (e.g., a Storage Area Network, orInternet Protocol network) with separate addresses. In the case of amulti-storage-system pod implementing a virtual storage system, allphysical storage systems associated with the pod may present themselvesas in some way the same storage system (e.g., as if the multiplephysical storage systems were no different than multiple network portsinto a single storage system).

Readers will appreciate that pods may also be units of administration,representing a collection of volumes, file systems, object/analyticstores, snapshots, and other administrative entities, where makingadministrative changes (e.g., name changes, property changes, managingexports or permissions for some part of the pod's dataset), on any onestorage system is automatically reflected to all active storage systemsassociated with the pod. In addition, pods could also be units of datacollection and data analysis, where performance and capacity metrics arepresented in ways that aggregate across all active storage systems forthe pod, or that call out data collection and analysis separately foreach pod, or perhaps presenting each attached storage system'scontribution to the incoming content and performance for each a pod.

One model for pod membership may be defined as a list of storagesystems, and a subset of that list where storage systems are consideredto be in-sync for the pod. A storage system may be considered to bein-sync for a pod if it is at least within a recovery of havingidentical idle content for the last written copy of the datasetassociated with the pod. Idle content is the content after anyin-progress modifications have completed with no processing of newmodifications. Sometimes this is referred to as “crash recoverable”consistency. Recovery of a pod carries out the process of reconcilingdifferences in applying concurrent updates to in-sync storage systems inthe pod. Recovery can resolve any inconsistencies between storagesystems in the completion of concurrent modifications that had beenrequested to various members of the pod but that were not signaled toany requestor as having completed successfully. Storage systems that arelisted as pod members but that are not listed as in-sync for the pod canbe described as “detached” from the pod. Storage systems that are listedas pod members, are in-sync for the pod, and are currently available foractively serving data for the pod are “online” for the pod.

Each storage system member of a pod may have its own copy of themembership, including which storage systems it last knew were in-sync,and which storage systems it last knew comprised the entire set of podmembers. To be online for a pod, a storage system must consider itselfto be in-sync for the pod and must be communicating with all otherstorage systems it considers to be in-sync for the pod. If a storagesystem can't be certain that it is in-sync and communicating with allother storage systems that are in-sync, then it must stop processing newincoming requests for the pod (or must complete them with an error orexception) until it can be certain that it is in-sync and communicatingwith all other storage systems that are in-sync. A first storage systemmay conclude that a second paired storage system should be detached,which will allow the first storage system to continue since it is nowin-sync with all storage systems now in the list. But, the secondstorage system must be prevented from concluding, alternatively, thatthe first storage system should be detached and with the second storagesystem continuing operation. This would result in a “split brain”condition that can lead to irreconcilable datasets, dataset corruption,or application corruption, among other dangers.

The situation of needing to determine how to proceed when notcommunicating with paired storage systems can arise while a storagesystem is running normally and then notices lost communications, whileit is currently recovering from some previous fault, while it isrebooting or resuming from a temporary power loss or recoveredcommunication outage, while it is switching operations from one set ofstorage system controller to another set for whatever reason, or duringor after any combination of these or other kinds of events. In fact, anytime a storage system that is associated with a pod can't communicatewith all known non-detached members, the storage system can either waitbriefly until communications can be established, go offline and continuewaiting, or it can determine through some means that it is safe todetach the non-communicating storage system without risk of incurring asplit brain due to the non-communicating storage system concluding thealternative view, and then continue. If a safe detach can happen quicklyenough, the storage system can remain online for the pod with littlemore than a short delay and with no resulting application outages forapplications that can issue requests to the remaining online storagesystems.

One example of this situation is when a storage system may know that itis out-of-date. That can happen, for example, when a first storagesystem is first added to a pod that is already associated with one ormore storage systems, or when a first storage system reconnects toanother storage system and finds that the other storage system hadalready marked the first storage system as detached. In this case, thisfirst storage system will simply wait until it connects to some otherset of storage systems that are in-sync for the pod.

This model demands some degree of consideration for how storage systemsare added to or removed from pods or from the in-sync pod members list.Since each storage system will have its own copy of the list, and sincetwo independent storage systems can't update their local copy at exactlythe same time, and since the local copy is all that is available on areboot or in various fault scenarios, care must be taken to ensure thattransient inconsistencies don't cause problems. For example, if onestorage systems is in-sync for a pod and a second storage system isadded, then if the second storage system is updated to list both storagesystems as in-sync first, then if there is a fault and a restart of bothstorage systems, the second might startup and wait to connect to thefirst storage system while the first might be unaware that it should orcould wait for the second storage system. If the second storage systemthen responds to an inability to connect with the first storage systemby going through a process to detach it, then it might succeed incompleting a process that the first storage system is unaware of,resulting in a split brain. As such, it may be necessary to ensure thatstorage systems won't disagree inappropriately on whether they might optto go through a detach process if they aren't communicating.

One way to ensure that storage systems won't disagree inappropriately onwhether they might opt to go through a detach process if they aren'tcommunicating is to ensure that when adding a new storage system to thein-sync member list for a pod, the new storage system first stores thatit is a detached member (and perhaps that it is being added as anin-sync member). Then, the existing in-sync storage systems can locallystore that the new storage system is an in-sync pod member before thenew storage system locally stores that same fact. If there is a set ofreboots or network outages prior to the new storage system storing itsin-sync status, then the original storage systems may detach the newstorage system due to non-communication, but the new storage system willwait. A reverse version of this change might be needed for removing acommunicating storage system from a pod: first the storage system beingremoved stores that it is no longer in-sync, then the storage systemsthat will remain store that the storage system being removed is nolonger in-sync, then all storage systems delete the storage system beingremoved from their pod membership lists. Depending on theimplementation, an intermediate persisted detached state may not benecessary. Whether or not care is required in local copies of membershiplists may depend on the model storage systems use for monitoring eachother or for validating their membership. If a consensus model is usedfor both, or if an external system (or an external distributed orclustered system) is used to store and validate pod membership, theninconsistencies in locally stored membership lists may not matter.

When communications fail or one or several storage systems in a podfail, or when a storage system starts up (or fails over to a secondarycontroller) and can't communicate with paired storage systems for a pod,and it is time for one or more storage systems to decide to detach oneor more paired storage systems, some algorithm or mechanism must beemployed to decide that it is safe to do so and to follow through on thedetach. One means of resolving detaches is use a majority (or quorum)model for membership. With three storage systems, as long as two arecommunicating, they can agree to detach a third storage system thatisn't communicating, but that third storage system cannot by itselfchoose to detach either of the other two. Confusion can arise whenstorage system communication is inconsistent. For example, storagesystem A might be communicating with storage system B but not C, whilestorage system B might be communicating with both A and C. So, A and Bcould detach C, or B and C could detach A, but more communicationbetween pod members may be needed to figure this out.

Care needs to be taken in a quorum membership model when adding andremoving storage systems. For example, if a fourth storage system isadded, then a “majority” of storage systems is at that point three. Thetransition from three storage systems (with two required for majority)to a pod including a fourth storage system (with three required formajority) may require something similar to the model describedpreviously for carefully adding a storage system to the in-sync list.For example, the fourth storage system might start in an attaching statebut not yet attached where it would never instigate a vote over quorum.Once in that state, the original three pod members could each be updatedto be aware of the fourth member and the new requirement for a threestorage system majority to detach a fourth. Removing a storage systemfrom a pod might similarly move that storage system to a locally stored“detaching” state before updating other pod members. A variant schemefor this is to use a distributed consensus mechanism such as PAXOS orRAFT to implement any membership changes or to process detach requests.

Another means of managing membership transitions is to use an externalsystem that is outside of the storage systems themselves to handle podmembership. In order to become online for a pod, a storage system mustfirst contact the external pod membership system to verify that it isin-sync for the pod. Any storage system that is online for a pod shouldthen remain in communication with the pod membership system and shouldwait or go offline if it loses communication. An external pod membershipmanager could be implemented as a highly available cluster using variouscluster tools, such as Oracle RAC, Linux HA, VERITAS Cluster Server,IBM's HACMP, or others. An external pod membership manager could alsouse distributed configuration tools such as Etcd or Zookeeper, or areliable distributed database such as Amazon's DynamoDB.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may receive a request to read a portion of the dataset (426,428) and process the request to read the portion of the dataset locallyaccording to some embodiments of the present disclosure. Readers willappreciate that although requests to modify (e.g., a write operation)the dataset (426, 428) require coordination between the storage systems(402, 404, 406) in a pod, as the dataset (426, 428) should be consistentacross all storage systems (402, 404, 406) in a pod, responding to arequest to read a portion of the dataset (426, 428) does not requiresimilar coordination between the storage systems (402, 404, 406). Assuch, a particular storage system that receives a read request mayservice the read request locally by reading a portion of the dataset(426, 428) that is stored within the storage system's storage devices,with no synchronous communication with other storage systems in the pod.Read requests received by one storage system for a replicated dataset ina replicated cluster are expected to avoid any communication in the vastmajority of cases, at least when received by a storage system that isrunning within a cluster that is also running nominally. Such readsshould normally be processed simply by reading from the local copy of aclustered dataset with no further interaction required with otherstorage systems in the cluster.

Readers will appreciate that the storage systems may take steps toensure read consistency such that a read request will return the sameresult regardless of which storage system processes the read request.For example, the resulting clustered dataset content for any set ofupdates received by any set of storage systems in the cluster should beconsistent across the cluster, at least at any time updates are idle(all previous modifying operations have been indicated as complete andno new update requests have been received and processed in any way).More specifically, the instances of a clustered dataset across a set ofstorage systems can differ only as a result of updates that have not yetcompleted. This means, for example, that any two write requests whichoverlap in their volume block range, or any combination of a writerequest and an overlapping snapshot, compare-and-write, or virtual blockrange copy, must yield a consistent result on all copies of the dataset.Two operations should not yield a result as if they happened in oneorder on one storage system and a different order on another storagesystem in the replicated cluster.

Furthermore, read requests can be made time order consistent. Forexample, if one read request is received on a replicated cluster andcompleted and that read is then followed by another read request to anoverlapping address range which is received by the replicated clusterand where one or both reads in any way overlap in time and volumeaddress range with a modification request received by the replicatedcluster (whether any of the reads or the modification are received bythe same storage system or a different storage system in the replicatedcluster), then if the first read reflects the result of the update thenthe second read should also reflect the results of that update, ratherthan possibly returning data that preceded the update. If the first readdoes not reflect the update, then the second read can either reflect theupdate or not. This ensures that between two read requests “time” for adata segment cannot roll backward.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also detect a disruption in data communications with oneor more of the other storage systems and determine whether to theparticular storage system should remain in the pod. A disruption in datacommunications with one or more of the other storage systems may occurfor a variety of reasons. For example, a disruption in datacommunications with one or more of the other storage systems may occurbecause one of the storage systems has failed, because a networkinterconnect has failed, or for some other reason. An important aspectof synchronous replicated clustering is ensuring that any fault handlingdoesn't result in unrecoverable inconsistencies, or any inconsistency inresponses. For example, if a network fails between two storage systems,at most one of the storage systems can continue processing newlyincoming I/O requests for a pod. And, if one storage system continuesprocessing, the other storage system can't process any new requests tocompletion, including read requests.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also determine whether the particular storage systemshould remain in the pod in response to detecting a disruption in datacommunications with one or more of the other storage systems. Asmentioned above, to be ‘online’ as part of a pod, a storage system mustconsider itself to be in-sync for the pod and must be communicating withall other storage systems it considers to be in-sync for the pod. If astorage system can't be certain that it is in-sync and communicatingwith all other storage systems that are in-sync, then it may stopprocessing new incoming requests to access the dataset (426, 428). Assuch, the storage system may determine whether to the particular storagesystem should remain online as part of the pod, for example, bydetermining whether it can communicate with all other storage systems itconsiders to be in-sync for the pod (e.g., via one or more testmessages), by determining whether the all other storage systems itconsiders to be in-sync for the pod also consider the storage system tobe attached to the pod, through a combination of both steps where theparticular storage system must confirm that it can communicate with allother storage systems it considers to be in-sync for the pod and thatall other storage systems it considers to be in-sync for the pod alsoconsider the storage system to be attached to the pod, or through someother mechanism.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also keep the dataset on the particular storage systemaccessible for management and dataset operations in response todetermining that the particular storage system should remain in the pod.The storage system may keep the dataset (426, 428) on the particularstorage system accessible for management and dataset operations, forexample, by accepting requests to access the version of the dataset(426, 428) that is stored on the storage system and processing suchrequests, by accepting and processing management operations associatedwith the dataset (426, 428) that are issued by a host or authorizedadministrator, by accepting and processing management operationsassociated with the dataset (426, 428) that are issued by one of theother storage systems, or in some other way.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may, however, make the dataset on the particular storagesystem inaccessible for management and dataset operations in response todetermining that the particular storage system should not remain in thepod. The storage system may make the dataset (426, 428) on theparticular storage system inaccessible for management and datasetoperations, for example, by rejecting requests to access the version ofthe dataset (426, 428) that is stored on the storage system, byrejecting management operations associated with the dataset (426, 428)that are issued by a host or other authorized administrator, byrejecting management operations associated with the dataset (426, 428)that are issued by one of the other storage systems in the pod, or insome other way.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also detect that the disruption in data communicationswith one or more of the other storage systems has been repaired and makethe dataset on the particular storage system accessible for managementand dataset operations. The storage system may detect that thedisruption in data communications with one or more of the other storagesystems has been repaired, for example, by receiving a message from theone or more of the other storage systems. In response to detecting thatthe disruption in data communications with one or more of the otherstorage systems has been repaired, the storage system may make thedataset (426, 428) on the particular storage system accessible formanagement and dataset operations once the previously detached storagesystem has been resynchronized with the storage systems that remainedattached to the pod.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also go offline from the pod such that the particularstorage system no longer allows management and dataset operations. Thedepicted storage systems (402, 404, 406) may go offline from the podsuch that the particular storage system no longer allows management anddataset operations for a variety of reasons. For example, the depictedstorage systems (402, 404, 406) may also go offline from the pod due tosome fault with the storage system itself, because an update or someother maintenance is occurring on the storage system, due tocommunications faults, or for many other reasons. In such an example,the depicted storage systems (402, 404, 406) may subsequently update thedataset on the particular storage system to include all updates to thedataset since the particular storage system went offline and go backonline with the pod such that the particular storage system allowsmanagement and dataset operations, as will be described in greaterdetail in the resynchronization sections included below.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also identifying a target storage system forasynchronously receiving the dataset, where the target storage system isnot one of the plurality of storage systems across which the dataset issynchronously replicated. Such a target storage system may represent,for example, a backup storage system, as some storage system that makesuse of the synchronously replicated dataset, and so on. In fact,synchronous replication can be leveraged to distribute copies of adataset closer to some rack of servers, for better local readperformance. One such case is smaller top-of-rack storage systemssymmetrically replicated to larger storage systems that are centrallylocated in the data center or campus and where those larger storagesystems are more carefully managed for reliability or are connected toexternal networks for asynchronous replication or backup services.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also identify a portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems and asynchronously replicate, to the targetstorage system, the portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems, wherein the two or more storage systemscollectively replicate the entire dataset to the target storage system.In such a way, the work associated with asynchronously replicating aparticular dataset may be split amongst the members of a pod, such thateach storage system in a pod is only responsible for asynchronouslyreplicating a subset of a dataset to the target storage system.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also detach from the pod, such that the particular storagesystem that detaches from the pod is no longer included in the set ofstorage systems across which the dataset is synchronously replicated.For example, if storage system (404) in FIG. 4 detached from the pod(430) illustrated in FIG. 4, the pod (430) would only include storagesystems (402, 406) as the storage systems across which the dataset (426)that is included in the pod (430) would be synchronously replicatedacross. In such an example, detaching the storage system from the podcould also include removing the dataset from the particular storagesystem that detached from the pod. Continuing with the example where thestorage system (404) in FIG. 4 detached from the pod (430) illustratedin FIG. 4, the dataset (426) that is included in the pod (430) could bedeleted or otherwise removed from the storage system (404).

Readers will appreciate that there are a number of unique administrativecapabilities enabled by the pod model that can further be supported.Also, the pod model itself introduces some issues that can be addressedby an implementation. For example, when a storage system is offline fora pod, but is otherwise running, such as because an interconnect failedand another storage system for the pod won out in mediation, there maystill be a desire or need to access the offline pod's dataset on theoffline storage system. One solution may be simply to enable the pod insome detached mode and allow the dataset to be accessed. However, thatsolution can be dangerous and that solution can cause the pod's metadataand data to be much more difficult to reconcile when the storage systemsdo regain communication. Furthermore, there could still be a separatepath for hosts to access the offline storage system as well as the stillonline storage systems. In that case, a host might issue I/O to bothstorage systems even though they are no longer being kept in sync,because the host sees target ports reporting volumes with the sameidentifiers and the host I/O drivers presume it sees additional paths tothe same volume. This can result in fairly damaging data corruption asreads and writes issued to both storage systems are no longer consistenteven though the host presumes they are. As a variant of this case, in aclustered application, such as a shared storage clustered database, theclustered application running on one host might be reading or writing toone storage system and the same clustered application running on anotherhost might be reading or writing to the “detached” storage system, yetthe two instances of the clustered application are communicating betweeneach other on the presumption that the dataset they each see is entirelyconsistent for completed writes. Since they aren't consistent, thatpresumption is violated and the application's dataset (e.g., thedatabase) can quickly end up being corrupted.

One way to solve both of these problems is to allow for an offline pod,or perhaps a snapshot of an offline pod, to be copied to a new pod withnew volumes that have sufficiently new identities that host I/O driversand clustered applications won't confuse the copied volumes as being thesame as the still online volumes on another storage system. Since eachpod maintains a complete copy of the dataset, which is crash consistentbut perhaps slightly different from the copy of the pod dataset onanother storage system, and since each pod has an independent copy ofall data and metadata needed to operate on the pod content, it is astraightforward problem to make a virtual copy of some or all volumes orsnapshots in the pod to new volumes in a new pod. In a logical extentgraph implementation, for example, all that is needed is to define newvolumes in a new pod which reference logical extent graphs from thecopied pod associated with the pod's volumes or snapshots, and with thelogical extent graphs being marked as copy on write. The new volumesshould be treated as new volumes, similarly to how volume snapshotscopied to a new volume might be implemented. Volumes may have the sameadministrative name, though within a new pod namespace. But, they shouldhave different underlying identifiers, and differing logical unitidentifiers from the original volumes.

In some cases it may be possible to use virtual network isolationtechniques (for example, by creating a virtual LAN in the case of IPnetworks or a virtual SAN in the case of fiber channel networks) in sucha way that isolation of volumes presented to some interfaces can beassured to be inaccessible from host network interfaces or host SCSIinitiator ports that might also see the original volumes. In such cases,it may be safe to provide the copies of volumes with the same SCSI orother storage identifiers as the original volumes. This could be used,for example, in cases where the applications expect to see a particularset of storage identifiers in order to function without an undue burdenin reconfiguration.

Some of the techniques described herein could also be used outside of anactive fault context to test readiness for handling faults. Readinesstesting (sometimes referred to as “fire drills”) is commonly requiredfor disaster recovery configurations, where frequent and repeatedtesting is considered a necessity to ensure that most or all aspects ofa disaster recovery plan are correct and account for any recent changesto applications, datasets, or changes in equipment. Readiness testingshould be non-disruptive to current production operations, includingreplication. In many cases the real operations can't actually be invokedon the active configuration, but a good way to get close is to usestorage operations to make copies of production datasets, and thenperhaps couple that with the use of virtual networking, to create anisolated environment containing all data that is believed necessary forthe important applications that must be brought up successfully in casesof disasters. Making such a copy of a synchronously replicated (or evenan asynchronously replicated) dataset available within a site (orcollection of sites) that is expected to perform a disaster recoveryreadiness test procedure and then starting the important applications onthat dataset to ensure that it can startup and function is a great tool,since it helps ensure that no important parts of the applicationdatasets were left out in the disaster recovery plan. If necessary, andpractical, this could be coupled with virtual isolated networks coupledperhaps with isolated collection of physical or virtual machines, to getas close as possible to a real world disaster recovery takeoverscenario. Virtually copying a pod (or set of pods) to another pod as apoint-in-time image of the pod datasets immediately creates an isolateddataset that contains all the copied elements and that can then beoperated on essentially identically to the originally pods, as well asallowing isolation to a single site (or a few sites) separately from theoriginal pod. Further, these are fast operations and they can be torndown and repeated easily allowing testing to repeated as often as isdesired.

Some enhancements could be made to get further toward perfect disasterrecovery testing. For example, in conjunction with isolated networks,SCSI logical unit identities or other types of identities could becopied into the target pod so that the test servers, virtual machines,and applications see the same identities. Further, the administrativeenvironment of the servers could be configured to respond to requestsfrom a particular virtual set of virtual networks to respond to requestsand operations on the original pod name so scripts don't require use oftest-variants with alternate “test” versions of object names. A furtherenhancement can be used in cases where the host-side serverinfrastructure that will take over in the case of a disaster takeovercan be used during a test. This includes cases where a disaster recoverydata center is completely stocked with alternative server infrastructurethat won't generally be used until directed to do so by a disaster. Italso includes cases where that infrastructure might be used fornon-critical operations (for example, running analytics on productiondata, or simply supporting application development or other functionswhich may be important but can be halted if needed for more criticalfunctions). Specifically, host definitions and configurations and theserver infrastructure that will use them can be set up as they will befor an actual disaster recovery takeover event and tested as part ofdisaster recovery takeover testing, with the tested volumes beingconnected to these host definitions from the virtual pod copy used toprovide a snapshot of the dataset. From the standpoint of the storagesystems involved, then, these host definitions and configurations usedfor testing, and the volume-to-host connection configurations usedduring testing, can be reused when an actual disaster takeover event istriggered, greatly minimizing the configuration differences between thetest configuration and the real configuration that will be used in caseof a disaster recovery takeover.

In some cases it may make sense to move volumes out of a first pod andinto a new second pod including just those volumes. The pod membershipand high availability and recovery characteristics can then be adjustedseparately, and administration of the two resulting pod datasets canthen be isolated from each other. An operation that can be done in onedirection should also be possible in the other direction. At some point,it may make sense to take two pods and merge them into one so that thevolumes in each of the original two pods will now track each other forstorage system membership and high availability and recoverycharacteristics and events. Both operations can be accomplished safelyand with reasonably minimal or no disruption to running applications byrelying on the characteristics suggested for changing mediation orquorum properties for a pod which were discussed in an earlier section.With mediation, for example, a mediator for a pod can be changed using asequence consisting of a step where each storage system in a pod ischanged to depend on both a first mediator and a second mediator andeach is then changed to depend only on the second mediator. If a faultoccurs in the middle of the sequence, some storage systems may depend onboth the first mediator and the second mediator, but in no case willrecovery and fault handling result in some storage systems dependingonly on the first mediator and other storage systems only depending onthe second mediator. Quorum can be handled similarly by temporarilydepending on winning against both a first quorum model and a secondquorum model in order to proceed to recovery. This may result in a veryshort time period where availability of the pod in the face of faultsdepend on additional resources, thus reducing potential availability,but this time period is very short and the reduction in availability isoften very little. With mediation, if the change in mediator parametersis nothing more than the change in the key used for mediation and themediation service used is the same, then the potential reduction inavailability is even less, since it now depends only on two calls to thesame service versus one call to that service, and rather than separatecalls to two separate services.

Readers will note that changing the quorum model may be quite complex.An additional step may be necessary where storage systems willparticipate in the second quorum model but won't depend on winning inthat second quorum model, which is then followed by the step of alsodepending on the second quorum model. This may be necessary to accountfor the fact that if only one system has processed the change to dependon the quorum model, then it will never win quorum since there willnever be a majority. With this model in place for changing the highavailability parameters (mediation relationship, quorum model, takeoverpreferences), we can create a safe procedure for these operations tosplit a pod into two or to join two pods into one. This may requireadding one other capability: linking a second pod to a first pod forhigh availability such that if two pods include compatible highavailability parameters the second pod linked to the first pod candepend on the first pod for determining and instigating detach-relatedprocessing and operations, offline and in-sync states, and recovery andresynchronization actions.

To split a pod into two, which is an operation to move some volumes intoa newly created pod, a distributed operation may be formed that can bedescribed as: form a second pod into which we will move a set of volumeswhich were previously in a first pod, copy the high availabilityparameters from the first pod into the second pod to ensure they arecompatible for linking, and link the second pod to the first pod forhigh availability. This operation may be encoded as messages and shouldbe implemented by each storage system in the pod in such a way that thestorage system ensures that the operation happens completely on thatstorage system or does not happen at all if processing is interrupted bya fault. Once all in-sync storage systems for the two pods haveprocessed this operation, the storage systems can then process asubsequent operation which changes the second pod so that it is nolonger linked to the first pod. As with other changes to highavailability characteristics for a pod, this involves first having eachin-sync storage system change to rely on both the previous model (thatmodel being that high availability is linked to the first pod) and thenew model (that model being its own now independent high availability).In the case of mediation or quorum, this means that storage systemswhich processed this change will first depend on mediation or quorumbeing achieved as appropriate for the first pod and will additionallydepend on a new separate mediation (for example, a new mediation key) orquorum being achieved for the second pod before the second pod canproceed following a fault that required mediation or testing for quorum.As with the previous description of changing quorum models, anintermediate step may set storage systems to participate in quorum forthe second pod before the step where storage systems participate in anddepend on quorum for the second pod. Once all in-sync storage systemshave processed the change to depend on the new parameters for mediationor quorum for both the first pod and the second pod, the split iscomplete.

Joining a second pod into a first pod operates essentially in reverse.First, the second pod must be adjusted to be compatible with the firstpod, by having an identical list of storage systems and by having acompatible high availability model. This may involve some set of stepssuch as those described elsewhere in this paper to add or remove storagesystems or to change mediator and quorum models. Depending onimplementation, it may be necessary only to reach an identical list ofstorage systems. Joining proceeds by processing an operation on eachin-sync storage system to link the second pod to the first pod for highavailability. Each storage system which processes that operation willthen depend on the first pod for high availability and then the secondpod for high availability. Once all in-sync storage systems for thesecond pod have processed that operation, the storage systems will theneach process a subsequent operation to eliminate the link between thesecond pod and the first pod, migrate the volumes from the second podinto the first pod, and delete the second pod. Host or applicationdataset access can be preserved throughout these operations, as long asthe implementation allows proper direction of host or applicationdataset modification or read operations to the volume by identity and aslong as the identity is preserved as appropriate to the storage protocolor storage model (for example, as long as logical unit identifiers forvolumes and use of target ports for accessing volumes are preserved inthe case of SCSI).

Migrating a volume between pods may present issues. If the pods have anidentical set of in-sync membership storage systems, then it may bestraightforward: temporarily suspend operations on the volumes beingmigrated, switch control over operations on those volumes to controllingsoftware and structures for the new pod, and then resume operations.This allows for a seamless migration with continuous uptime forapplications apart from the very brief operation suspension, providednetwork and ports migrate properly between pods. Depending on theimplementation, suspending operations may not even be necessary, or maybe so internal to the system that the suspension of operations has noimpact. Copying volumes between pods with different in-sync membershipsets is more of a problem. If the target pod for the copy has a subsetof in-sync members from the source pod, this isn't much of a problem: amember storage system can be dropped safely enough without having to domore work. But, if the target pod adds in-sync member storage systems tothe volume over the source pod, then the added storage systems must besynchronized to include the volume's content before they can be used.Until synchronized, this leaves the copied volumes distinctly differentfrom the already synchronized volumes, in that fault handling differsand request handling from the not yet synced member storage systemseither won't work or must be forwarded or won't be as fast because readswill have to traverse an interconnect. Also, the internal implementationwill have to handle some volumes being in sync and ready for faulthandling and others not being in sync.

There are other problems relating to reliability of the operation in theface of faults. Coordinating a migration of volumes betweenmulti-storage-system pods is a distributed operation. If pods are theunit of fault handling and recovery, and if mediation or quorum orwhatever means are used to avoid split-brain situations, then a switchin volumes from one pod with a particular set of state andconfigurations and relationships for fault handling, recovery, mediationand quorum to another then storage systems in a pod have to be carefulabout coordinating changes related to that handling for any volumes.Operations can't be atomically distributed between storage systems, butmust be staged in some way. Mediation and quorum models essentiallyprovide pods with the tools for implementing distributed transactionalatomicity, but this may not extend to inter-pod operations withoutadding to the implementation.

Consider even a simple migration of a volume from a first pod to asecond pod even for two pods that share the same first and secondstorage systems. At some point the storage systems will coordinate todefine that the volume is now in the second pod and is no longer in thefirst pod. If there is no inherent mechanism for transactional atomicityacross the storage systems for the two pods, then a naive implementationcould leave the volume in the first pod on the first storage system andthe second pod on the second storage system at the time of a networkfault that results in fault handling to detach storage systems from thetwo pods. If pods separately determine which storage system succeeds indetaching the other, then the result could be that the same storagesystem detaches the other storage system for both pods, in which casethe result of the volume migration recovery should be consistent, or itcould result in a different storage system detaching the other for thetwo pods. If the first storage system detaches the second storage systemfor the first pod and the second storage system detaches the firststorage system for the second pod, then recovery might result in thevolume being recovered to the first pod on the first storage system andinto the second pod on the second storage system, with the volume thenrunning and exported to hosts and storage applications on both storagesystems. If instead the second storage system detaches the first storagesystem for the first pod and first storage detaches the second storagesystem for the second pod, then recovery might result in the volumebeing discarded from the second pod by the first storage system and thevolume being discarded from the first pod by the second storage system,resulting in the volume disappearing entirely. If the pods a volume isbeing migrated between are on differing sets of storage systems, thenthings can get even more complicated.

A solution to these problems may be to use an intermediate pod alongwith the techniques described previously for splitting and joining pods.This intermediate pod may never be presented as visible managed objectsassociated with the storage systems. In this model, volumes to be movedfrom a first pod to a second pod are first split from the first pod intoa new intermediate pod using the split operation described previously.The storage system members for the intermediate pod can then be adjustedto match the membership of storage systems by adding or removing storagesystems from the pod as necessary. Subsequently, the intermediate podcan be joined with the second pod.

For further explanation, FIG. 5 sets forth a flow chart illustratingsteps that may be performed by storage systems (402, 404, 406) thatsupport a pod according to some embodiments of the present disclosure.Although depicted in less detail, the storage systems (402. 404, 406)depicted in FIG. 5 may be similar to the storage systems described abovewith reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, FIG. 4, or anycombination thereof In fact, the storage systems (402, 404, 406)depicted in FIG. 5 may include the same, fewer, additional components asthe storage systems described above.

In the example method depicted in FIG. 5, a storage system (402) mayattach (508) to a pod. The model for pod membership may include a listof storage systems and a subset of that list where storage systems arepresumed to be in-sync for the pod. A storage system is in-sync for apod if it is at least within a recovery of having identical idle contentfor the last written copy of the dataset associated with the pod. Idlecontent is the content after any in-progress modifications havecompleted with no processing of new modifications. Sometimes this isreferred to as “crash recoverable” consistency. Storage systems that arelisted as pod members but that are not listed as in-sync for the pod canbe described as “detached” from the pod. Storage systems that are listedas pod members, are in-sync for the pod, and are currently available foractively serving data for the pod are “online” for the pod.

In the example method depicted in FIG. 5, the storage system (402) mayattach (508) to a pod, for example, by synchronizing its locally storedversion of the dataset (426) along with an up-to-date version of thedataset (426) that is stored on other storage systems (404, 406) in thepod that are online, as the term is described above. In such an example,in order for the storage system (402) to attach (508) to the pod, a poddefinition stored locally within each of the storage systems (402, 404,406) in the pod may need to be updated in order for the storage system(402) to attach (508) to the pod. In such an example, each storagesystem member of a pod may have its own copy of the membership,including which storage systems it last knew were in-sync, and whichstorage systems it last knew comprised the entire set of pod members.

In the example method depicted in FIG. 5, the storage system (402) mayalso receive (510) a request to read a portion of the dataset (426) andthe storage system (402) may process (512) the request to read theportion of the dataset (426) locally. Readers will appreciate thatalthough requests to modify (e.g., a write operation) the dataset (426)require coordination between the storage systems (402, 404, 406) in apod, as the dataset (426) should be consistent across all storagesystems (402, 404, 406) in a pod, responding to a request to read aportion of the dataset (426) does not require similar coordinationbetween the storage systems (402, 404, 406). As such, a particularstorage system (402) that receives a read request may service the readrequest locally by reading a portion of the dataset (426) that is storedwithin the storage system's (402) storage devices, with no synchronouscommunication with other storage systems (404, 406) in the pod. Readrequests received by one storage system for a replicated dataset in areplicated cluster are expected to avoid any communication in the vastmajority of cases, at least when received by a storage system that isrunning within a cluster that is also running nominally. Such readsshould normally be processed simply by reading from the local copy of aclustered dataset with no further interaction required with otherstorage systems in the cluster.

Readers will appreciate that the storage systems may take steps toensure read consistency such that a read request will return the sameresult regardless of which storage system processes the read request.For example, the resulting clustered dataset content for any set ofupdates received by any set of storage systems in the cluster should beconsistent across the cluster, at least at any time updates are idle(all previous modifying operations have been indicated as complete andno new update requests have been received and processed in any way).More specifically, the instances of a clustered dataset across a set ofstorage systems can differ only as a result of updates that have not yetcompleted. This means, for example, that any two write requests whichoverlap in their volume block range, or any combination of a writerequest and an overlapping snapshot, compare-and-write, or virtual blockrange copy, must yield a consistent result on all copies of the dataset.Two operations cannot yield a result as if they happened in one order onone storage system and a different order on another storage system inthe replicated cluster.

Furthermore, read requests may be time order consistent. For example, ifone read request is received on a replicated cluster and completed andthat read is then followed by another read request to an overlappingaddress range which is received by the replicated cluster and where oneor both reads in any way overlap in time and volume address range with amodification request received by the replicated cluster (whether any ofthe reads or the modification are received by the same storage system ora different storage system in the replicated cluster), then if the firstread reflects the result of the update then the second read should alsoreflect the results of that update, rather than possibly returning datathat preceded the update. If the first read does not reflect the update,then the second read can either reflect the update or not. This ensuresthat between two read requests “time” for a data segment cannot rollbackward.

In the example method depicted in FIG. 5, the storage system (402) mayalso detect (514) a disruption in data communications with one or moreof the other storage systems (404, 406). A disruption in datacommunications with one or more of the other storage systems (404, 406)may occur for a variety of reasons. For example, a disruption in datacommunications with one or more of the other storage systems (404, 406)may occur because one of the storage systems (402, 404, 406) has failed,because a network interconnect has failed (e.g., some portion of datacommunications link (502), data communications link (504), or datacommunications link (506) has failed), or for some other reason. Animportant aspect of synchronous replicated clustering is ensuring thatany fault handling doesn't result in unrecoverable inconsistencies, orany inconsistency in responses. For example, if a network fails betweentwo storage systems, at most one of the storage systems can continueprocessing newly incoming I/O requests for a pod. And, if one storagesystem continues processing, the other storage system can't process anynew requests to completion, including read requests.

In the example method depicted in FIG. 5, the storage system (402) mayalso determine (516) whether to the particular storage system (402)should remain online as part of the pod. As mentioned above, to be‘online’ as part of a pod, a storage system must consider itself to bein-sync for the pod and must be communicating with all other storagesystems it considers to be in-sync for the pod. If a storage systemcan't be certain that it is in-sync and communicating with all otherstorage systems that are in-sync, then it may stop processing newincoming requests to access the dataset (426). As such, the storagesystem (402) may determine (516) whether to the particular storagesystem (402) should remain online as part of the pod, for example, bydetermining whether it can communicate with all other storage systems(404, 406) it considers to be in-sync for the pod (e.g., via one or moretest messages), by determining whether the all other storage systems(404, 406) it considers to be in-sync for the pod also consider thestorage system (402) to be attached to the pod, through a combination ofboth steps where the particular storage system (402) must confirm thatit can communicate with all other storage systems (404, 406) itconsiders to be in-sync for the pod and that all other storage systems(404, 406) it considers to be in-sync for the pod also consider thestorage system (402) to be attached to the pod, or through some othermechanism.

In the example method depicted in FIG. 5, the storage system (402) mayalso, responsive to affirmatively (518) determining that the particularstorage system (402) should remain online as part of the pod, keep (522)the dataset (426) on the particular storage system (402) accessible formanagement and dataset operations. The storage system (402) may keep(522) the dataset (426) on the particular storage system (402)accessible for management and dataset operations, for example, byaccepting requests to access the version of the dataset (426) that isstored on the storage system (402) and processing such requests, byaccepting and processing management operations associated with thedataset (426) that are issued by a host or authorized administrator, byaccepting and processing management operations associated with thedataset (426) that are issued by one of the other storage systems (404,406) in the pod, or in some other way.

In the example method depicted in FIG. 5, the storage system (402) mayalso, responsive to determining that the particular storage systemshould not (520) remain online as part of the pod, make (524) thedataset (426) on the particular storage system (402) inaccessible formanagement and dataset operations. The storage system (402) may make(524) the dataset (426) on the particular storage system (402)inaccessible for management and dataset operations, for example, byrejecting requests to access the version of the dataset (426) that isstored on the storage system (402), by rejecting management operationsassociated with the dataset (426) that are issued by a host or otherauthorized administrator, by rejecting management operations associatedwith the dataset (426) that are issued by one of the other storagesystems (404, 406) in the pod, or in some other way.

In the example method depicted in FIG. 5, the storage system (402) mayalso detect (526) that the disruption in data communications with one ormore of the other storage systems (404, 406) has been repaired. Thestorage system (402) may detect (526) that the disruption in datacommunications with one or more of the other storage systems (404, 406)has been repaired, for example, by receiving a message from the one ormore of the other storage systems (404, 406). In response to detecting(526) that the disruption in data communications with one or more of theother storage systems (404, 406) has been repaired, the storage system(402) may make (528) the dataset (426) on the particular storage system(402) accessible for management and dataset operations.

Readers will appreciate that the example depicted in FIG. 5 describes anembodiment in which various actions are depicted as occurring withinsome order, although no ordering is required. Furthermore, otherembodiments may exist where the storage system (402) only carries out asubset of the described actions. For example, the storage system (402)may perform the steps of detecting (514) a disruption in datacommunications with one or more of the other storage systems (404, 406),determining (516) whether to the particular storage system (402) shouldremain in the pod, keeping (522) the dataset (426) on the particularstorage system (402) accessible for management and dataset operations ormaking (524) the dataset (426) on the particular storage system (402)inaccessible for management and dataset operations without firstreceiving (510) a request to read a portion of the dataset (426) andprocessing (512) the request to read the portion of the dataset (426)locally. Furthermore, the storage system (402) may detect (526) that thedisruption in data communications with one or more of the other storagesystems (404, 406) has been repaired and make (528) the dataset (426) onthe particular storage system (402) accessible for management anddataset operations without first receiving (510) a request to read aportion of the dataset (426) and processing (512) the request to readthe portion of the dataset (426) locally. In fact, none of the stepsdescribed herein are explicitly required in all embodiments asprerequisites for performing other steps described herein.

For further explanation, FIG. 6 sets forth a flow chart illustratingsteps that may be performed by storage systems (402, 404, 406) thatsupport a pod according to some embodiments of the present disclosure.Although depicted in less detail, the storage systems (402. 404, 406)depicted in FIG. 6 may be similar to the storage systems described abovewith reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, FIG. 4, or anycombination thereof In fact, the storage systems (402, 404, 406)depicted in FIG. 6 may include the same, fewer, additional components asthe storage systems described above and each of the storage systems maybe configured for data communications via one or more datacommunications links (502, 614, 616).

In the example method depicted in FIG. 6, two or more of the storagesystems (402, 404) may each identify (608) a target storage system (618)for asynchronously receiving the dataset (426). The target storagesystem (618) for asynchronously receiving the dataset (426) may beembodied, for example, as a backup storage system that is located in adifferent data center than either of the storage systems (402, 404) thatare members of a particular pod, as cloud storage that is provided by acloud services provider, or in many other ways. Readers will appreciatethat the target storage system (618) is not one of the plurality ofstorage systems (402, 404) across which the dataset (426) issynchronously replicated, and as such, the target storage system (618)initially does not include an up-to-date local copy of the dataset(426).

In the example method depicted in FIG. 6, two or more of the storagesystems (402, 404) may each also identify (610) a portion of the dataset(426) that is not being asynchronously replicated to the target storagesystem (618) by any of the other storages systems that are members of apod that includes the dataset (426). In such an example, the storagesystems (402, 404) may each asynchronously replicate (612), to thetarget storage system (618), the portion of the dataset (426) that isnot being asynchronously replicated to the target storage system by anyof the other storages systems. Consider an example in which a firststorage system (402) is responsible for asynchronously replicating afirst portion (e.g., a first half of an address space) of the dataset(426) to the target storage system (618). In such an example, the secondstorage system (404) would be responsible for asynchronously replicatinga second portion (e.g., a second half of an address space) of thedataset (426) to the target storage system (618), such that the two ormore storage systems (402, 404) collectively replicate the entiredataset (426) to the target storage system (618).

Readers will appreciate that through the use of pods, as describedabove, the replication relationship between two storage systems may beswitched from a relationship where data is asynchronously replicated toa relationship where data is synchronously replicated. For example, ifstorage system A is configured to asynchronously replicate a dataset tostorage system B, creating a pod that includes the dataset, storagesystem A as a member, and storage system B as a member can switch therelationship where data is asynchronously replicated to a relationshipwhere data is synchronously replicated. Likewise, through the use ofpods, the replication relationship between two storage systems may beswitched from a relationship where data is synchronously replicated to arelationship where data is asynchronously replicated. For example, if apod is created that includes the dataset, storage system A as a member,and storage system B as a member, by merely unstretching the pod (toremove storage system A as a member or to remove storage system B as amember), a relationship where data is synchronously replicated betweenthe storage systems can immediately be switched to a relationship wheredata is asynchronously replicated. In such a way, storage systems mayswitch back-and-forth as needed between asynchronous replication andsynchronous replication.

This switching can be facilitated by the implementation relying onsimilar techniques for both synchronous and asynchronous replication.For example, if resynchronization for a synchronously replicated datasetrelies on the same or a compatible mechanism as is used for asynchronousreplication, then switching to asynchronous replication is conceptuallyidentical to dropping the in-sync state and leaving a relationship in astate similar to a “perpetual recovery” mode. Likewise, switching fromasynchronous replication to synchronous replication can operateconceptually by “catching up” and becoming in-sync just as is done whencompleting a resynchronization with the switching system becoming anin-sync pod member.

Alternatively, or additionally, if both synchronous and asynchronousreplication rely on similar or identical common metadata, or a commonmodel for representing and identifying logical extents or stored blockidentities, or a common model for representing content-addressablestored blocks, then these aspects of commonality can be leveraged todramatically reduce the content that may need to be transferred whenswitching to and from synchronous and asynchronous replication. Further,if a dataset is asynchronously replicated from a storage system A to astorage system B, and system B further asynchronously replicates thatdata set to a storage system C, then a common metadata model, commonlogical extent or block identities, or common representation ofcontent-addressable stored blocks, can dramatically reduce the datatransfers needed to enable synchronous replication between storagesystem A and storage system C.

Readers will further appreciate that that through the use of pods, asdescribed above, replication techniques may be used to perform tasksother than replicating data. In fact, because a pod may include a set ofmanaged objects, tasks like migrating a virtual machine may be carriedout using pods and the replication techniques described herein. Forexample, if virtual machine A is executing on storage system A, bycreating a pod that includes virtual machine A as a managed object,storage system A as a member, and storage system B as a member, virtualmachine A and any associated images and definitions may be migrated tostorage system B, at which time the pod could simply be destroyed,membership could be updated, or other actions may be taken as necessary.

For further explanation, FIG. 7 sets forth a flow chart illustrating anexample method of establishing a synchronous replication relationshipbetween two or more storage systems (714, 724, 728) according to someembodiments of the present disclosure. Although depicted in less detail,the storage systems (714, 724, 728) depicted in FIG. 7 may be similar tothe storage systems described above with reference to FIGS. 1A-1D, FIGS.2A-2G, FIGS. 3A-3B, or any combination thereof. In fact, the storagesystems (714, 724, 728) depicted in FIG. 7 may include the same, fewer,additional components as the storage systems described above.

The example method depicted in FIG. 7 includes identifying (702), for adataset (712), a plurality of storage systems (714, 724, 728) acrosswhich the dataset (712) will be synchronously replicated. The dataset(712) depicted in FIG. 7 may be embodied, for example, as the contentsof a particular volume, as the contents of a particular shard of avolume, or as any other collection of one or more data elements. Thedataset (712) may be synchronized across a plurality of storage systems(714, 724, 728) such that each storage system (714, 724, 728) retains alocal copy of the dataset (712). In the examples described herein, sucha dataset (712) is synchronously replicated across the storage systems(714, 724, 728) in such a way that the dataset (712) can be accessedthrough any of the storage systems (714, 724, 728) with performancecharacteristics such that any one storage system in the cluster doesn'toperate substantially more optimally than any other storage system inthe cluster, at least as long as the cluster and the particular storagesystem being accessed are running nominally. In such systems,modifications to the dataset (712) should be made to the copy of thedataset that resides on each storage system (714, 724, 728) in such away that accessing the dataset (712) on any of the storage systems (714,724, 728) will yield consistent results. For example, a write requestissued to the dataset must be serviced on all storage systems (714, 724,728) or serviced on none of the storage systems (714, 724, 728).Likewise, some groups of operations (e.g., two write operations that aredirected to same location within the dataset) must be executed in thesame order on all storage systems (714, 724, 728) such that the copy ofthe dataset that resides on each storage system (714, 724, 728) isultimately identical. Modifications to the dataset (712) need not bemade at the exact same time, but some actions (e.g., issuing anacknowledgement that a write request directed to the dataset, enablingread access to a location within the dataset that is targeted by a writerequest that has not yet been completed on all storage systems) may bedelayed until the copy of the dataset (712) on each storage system (714,724, 728) has been modified.

In the example method depicted in FIG. 7, identifying (702), for adataset (712), a plurality of storage systems (714, 724, 728) acrosswhich the dataset (712) will be synchronously replicated may be carriedout, for example, by examining a pod definition or similar datastructure that associates a dataset (712) with one or more storagesystems (714, 724, 728) which nominally store that dataset (712). A‘pod’, as the term is used here and throughout the remainder of thepresent application, may be embodied as a management entity thatrepresents a dataset, a set of managed objects and managementoperations, a set of access operations to modify or read the dataset,and a plurality of storage systems. Such management operations maymodify or query managed objects equivalently through any of the storagesystems, where access operations to read or modify the dataset operateequivalently through any of the storage systems. Each storage system maystore a separate copy of the dataset as a proper subset of the datasetsstored and advertised for use by the storage system, where operations tomodify managed objects or the dataset performed and completed throughany one storage system are reflected in subsequent management objects toquery the pod or subsequent access operations to read the dataset.Additional details regarding a ‘pod’ may be found in previously filedprovisional patent application No. 62/518,071, which is incorporatedherein by reference. In such an example, the pod definition may includeat least an identification of a dataset (712) and a set of storagesystems (714, 724, 728) across which the dataset (712) is synchronouslyreplicated. Such a pod may encapsulate some of number of (perhapsoptional) properties including symmetric access, flexibleaddition/removal of replicas, high availability data consistency,uniform user administration across storage systems in relationship tothe dataset, managed host access, application clustering, and so on.Storage systems can be added to a pod, resulting in the pod's dataset(712) being copied to that storage system and then kept up to date asthe dataset (712) is modified. Storage systems can also be removed froma pod, resulting in the dataset (712) being no longer kept up to date onthe removed storage system. In such examples, a pod definition orsimilar data structure may be updated as storage systems are added toand removed from a particular pod.

The example method depicted in FIG. 7 also includes configuring (704)one or more data communications links (716, 718, 720) between each ofthe plurality of storage systems (714, 724, 728) to be used forsynchronously replicating the dataset (712). In the example methoddepicted in FIG. 6, the storage systems (714, 724, 728) in a pod mustcommunicate with each other both for high bandwidth data transfer, andfor cluster, status, and administrative communication. These distincttypes of communication could be over the same data communications links(716, 718, 720) or, in an alternative embodiment, these distinct typesof communication could be over separate data communications links (716,718, 720). In a cluster of dual controller storage systems, bothcontrollers in each storage system should have the nominal ability tocommunicate with both controllers for any paired storage systems (i.e.,any other storage system in a pod).

In a primary/secondary controller design, all cluster communication foractive replication may run between primary controllers until a faultoccurs. In such systems, some communication may occur between a primarycontroller and a secondary controller, or between secondary controllerson distinct storage systems, in order to verify that the datacommunications links between such entities are operational. In othercases, virtual network addresses might be used to limit theconfiguration needed for of inter-datacenter network links, or tosimplify design of the clustered aspect of the storage system. In anactive/active controller design, cluster communications might run fromall active controllers of one storage system to some or all activecontrollers in any paired storage systems, or they might be filteredthrough a common switch, or they might use a virtual network address tosimplify configuration, or they might use some combination. In ascale-out design, two or more common network switches may be used suchthat all scale-out storage controllers within the storage system connectto the network switches in order to handle data traffic. The switchesmight or might not use techniques to limit the number of exposed networkaddresses, so that paired storage systems don't need to be configuredwith the network addresses of all storage controllers.

In the example method depicted in FIG. 7, configuring (704) one or moredata communications links (716, 718, 720) between each of the pluralityof storage systems (714, 724, 728) to be used for synchronouslyreplicating the dataset (712) may be carried out, for example, byconfiguring the storage systems (716, 718, 720) to communicate viadefined ports over a data communications network, by configuring thestorage systems (716, 718, 720) to communicate over a point-to-pointdata communications link between two of the storage systems (716, 724,728), or in a variety of ways. If secure communication is required, someform of key exchange may be needed, or communication could be done orbootstrapped through some service such as SSH (Secure SHell), SSL, orsome other service or protocol built around public keys orDiffie-Hellman key exchange or reasonable alternatives. Securecommunications could also be mediated through some vendor-provided cloudservice tied in some way to customer identities. Alternately, a serviceconfigured to run on customer facilities, such as running in a virtualmachine or container, could be used to mediate key exchanges necessaryfor secure communications between replicating storage systems (716, 718,720). Readers will appreciate that a pod including more than two storagesystems may need communication links between most or all of theindividual storage systems. In the example depicted in FIG. 6, threedata communications links (716, 718, 720) are illustrated, althoughadditional data communications links may exist in other embodiments.

Readers will appreciate that communication between the storage systems(714, 724, 728) across which the dataset (712) will be synchronouslyreplicated serves some number of purposes. One purpose, for example, isto deliver data from one storage system (714, 724, 728) to anotherstorage system (714, 724, 728) as part of I/O processing. For example,processing a write commonly requires delivering the write content andsome description of the write to any paired storage systems for a pod.Another purpose served by data communications between the storagesystems (714, 724, 728) may be to communicate configuration changes andanalytics data in order to handle creating, extending, deleting orrenaming volumes, files, object buckets, and so on. Another purposeserved by data communications between the storage systems (714, 724,728) may be to carry out communication involved in detecting andhandling storage system and interconnect faults. This type ofcommunication may be time critical and may need to be prioritized toensure it doesn't get stuck behind a long network queue delay when alarge burst of write traffic is suddenly dumped on the datacenterinterconnect.

Readers will further appreciate that different types of communicationmay use the same connections, or different connections, and may use thesame networks, or different networks, in various combinations. Further,some communications may be encrypted and secured while othercommunications might not be encrypted. In some cases, the datacommunications links could be used to forward I/O requests (eitherdirectly as the requests themselves or as logical descriptions of theoperations the I/O requests represent) from one storage system toanother. This could be used, for example, in cases where one storagesystem has up-to-date and in-sync content for a pod, and another storagesystem does not currently have up-to-date and in-sync content for thepod. In such cases, as long as the data communications links arerunning, requests can be forwarded from the storage system that is notup-to-date and in-sync to the storage system that is up-to-date andin-sync.

The example method depicted in FIG. 7 also includes exchanging (706),between the plurality of storage systems (714, 724, 728), timinginformation (710, 722, 726) for at least one of the plurality of storagesystems (714, 724, 728). In the example method depicted in FIG. 6,timing information (710, 722, 726) for a particular storage system (714,724, 728) may be embodied, for example, as the value of a clock withinthe storage system (714, 724, 728). In an alternative embodiment, thetiming information (710, 722, 726) for a particular storage system (714,724, 728) may be embodied as a value which serves as a proxy for a clockvalue. The value which serves as a proxy for a clock value may beincluded in a token that is exchanged between the storage systems. Sucha value which serves as a proxy for a clock value may be embodied, forexample, a sequence number that a particular storage system (714, 724,728) or storage system controller can internally record as having beensent at a particular time. In such an example, if the token (e.g., thesequence number) is received back, the associated clock value can befound and utilized as the basis for determining whether a valid lease isstill in place. In the example method depicted in FIG. 6, exchanging(706) timing information (710, 722, 726) for at least one of theplurality of storage systems (714, 724, 728) between the plurality ofstorage systems (714, 724, 728) may be carried out, for example, by eachstorage system (714, 724, 728) sending timing information to each otherstorage system (714, 724, 728) in a pod on a periodic basis, on demand,within a predetermined amount of time after a lease is established,within a predetermined amount of time before a lease is set to expire,as part of an attempt to initiate or re-establish a synchronousreplication relationship, or in some other way.

The example method depicted in FIG. 7 also includes establishing (708),in dependence upon the timing information (710, 722, 726) for at leastone of the plurality of storage systems (714, 724, 728), a synchronousreplication lease, the synchronous replication lease identifying aperiod of time during which the synchronous replication relationship isvalid. In the example method depicted in FIG. 7, a synchronousreplication relationship is formed as a set of storage systems (714,724, 728) that replicate some dataset (712) between these largelyindependent stores, where each storage systems (714, 724, 728) has itsown copy and its own separate internal management of relevant datastructures for defining storage objects, for mapping objects to physicalstorage, for deduplication, for defining the mapping of content tosnapshots, and so on. A synchronous replication relationship can bespecific to a particular dataset, such that a particular storage system(714, 724, 728) may be associated with more than one synchronousreplication relationship, where each synchronous replicationrelationship is differentiated by the dataset being described and mayfurther consist of a different set of additional member storage systems.

In the example method depicted in FIG. 7, a synchronous replicationlease may be established (708) in dependence upon the timing information(710, 722, 726) for at least one of the plurality of storage systems(714, 724, 728) in a variety of different ways. In one embodiment, thestorage systems may establish (708) a synchronous replication lease byutilizing the timing information (710, 722, 726) for each of theplurality of storage systems (714, 724, 728) to coordinate clocks. Insuch an example, once the clocks are coordinated for each of the storagesystems (714, 724, 728), the storage system may establish (708) asynchronous replication lease that extends for a predetermined period oftime beyond the coordinated clock values. For example, if the clocks foreach storage system (714, 724, 728) are coordinated to be at a value ofX, the storage systems (714, 724, 728) may each be configured toestablish a synchronous replication lease that is valid until X+2seconds.

In an alternative embodiment, the need to coordinate clocks between thestorage systems (714, 724, 728) may be avoided while still achieving atiming guarantee. In such an embodiment, a storage controller withineach storage system (714, 724, 728) may have a local monotonicallyincreasing clock. A synchronous replication lease may be established(708) between storage controllers (such as a primary controller in onestorage system communicating with a primary controller in a pairedstorage system) by each controller sending its clock value to the otherstorage controllers along with the last clock value it received from theother storage controller. When a particular controller receives back itsclock value from another controller, it adds some agreed upon leaseinterval to that received clock value and uses that to establish (708)its local synchronous replication lease. In such a way, the synchronousreplication lease may be calculated in dependence upon a value of alocal clock that was received from another storage system.

Consider an example in which a storage controller in a first storagesystem (714) is communicating with a storage controller in a secondstorage system (724). In such an example, assume that the value of themonotonically increasing clock for the storage controller in the firststorage system (714) is 1000 milliseconds. Further assume that thestorage controller in the first storage system (714) sends a message tothe storage controller in the second storage system (724) indicatingthat its clock value at the time that the message was generated was 1000milliseconds. In such an example, assume that 500 milliseconds after thestorage controller in the first storage system (714) sent a message tothe storage controller in the second storage system (724) indicatingthat its clock value at the time that the message was generated was 1000milliseconds, the storage controller in the first storage system (714)receives a message from the storage controller in a second storagesystem (724) indicating that: 1) the value of the monotonicallyincreasing clock in the storage controller in the second storage system(724) was at a value of 5000 milliseconds when the message wasgenerated, and 2) the last value of the monotonically increasing clockin the storage controller in the first storage system (714) that wasreceived by the second storage system (724) was 1000 milliseconds. Insuch an example, if the agreed upon lease interval is 2000 milliseconds,the first storage system (714) will establish (708) a synchronousreplication lease that is valid until the monotonically increasing clockfor the storage controller in the first storage system (714) is at avalue of 3000 milliseconds. If the storage controller in the firststorage system (714) does not receive a message from the storagecontroller in the second storage system (724) that includes an updatedvalue of the monotonically increasing clock for the storage controllerin the first storage system (714) by the time that the monotonicallyincreasing clock for the storage controller in the first storage system(714) reaches a value of 3000 milliseconds, the first storage system(714) will treat the synchronous replication lease to have expired andmay take various actions as described in greater detail below. Readerswill appreciate that storage controllers within the remaining storagesystems (724, 728) in a pod may react similarly and perform a similartracking and updating of the synchronous replication lease. Essentially,the receiving controller can be assured that the network and the pairedcontrollers were running somewhere during that time interval, and it canbe assured that the paired controller received a message that it sentsomewhere during that time interval. Without any coordination in clocks,the receiving controller can't know exactly where in that time intervalthe network and the paired controller were running, and can't reallyknow if there were queue delays in sending its clock value or inreceiving back its clock value.

In a pod consisting of two storage systems, each with a simple primarycontroller, where the primary controllers are exchanging clocks as partof their cluster communication, each primary controller can use theactivity lease to put a bound on when it won't know for certain that thepaired controller was running. At the point it becomes uncertain (whenthe controller's connection's activity lease has expired), it can startsending messages indicating that it is uncertain and that a properlysynchronized connection must be reestablished before activity leases canagain be resumed. These messages may be received and responses may notbe received, if the network is working in one direction but is notworking properly in the other direction. This may be the firstindication by a running paired controller that the connection isn'trunning normally, because its own activity lease may not yet haveexpired, due to a different combination of lost messages and queuedelays. As a result, if such a message is received, it should alsoconsider its own activity lease to be expired, and it should startsending messages of its own attempting to coordinate synchronizing theconnection and resuming of activity leases. Until that happens and a newset of clock exchanges can succeed, neither controller can consider itsactivity lease to be valid.

In this model, a controller can wait for lease interval seconds after itstarted sending reestablish messages, and if it hasn't received aresponse, it can be assured that either the paired controller is down orthe paired controller's own lease for the connection will have expired.To handle minor amounts of clock drift, it may wait slightly longer thanthe lease interval (i.e., a reestablishment lease). When a controllerreceives a reestablish message, it could consider the reestablishmentlease to be expired immediately, rather than waiting (since it knowsthat the sending controller's activity lease has expired), but it willoften make sense to attempt further messaging before giving up, in casemessage loss was a temporary condition caused, for example, by acongested network switch.

In an alternative embodiment, in addition to establishing a synchronousreplication lease, a cluster membership lease may also be establishedupon receipt of a clock value from a paired storage system or uponreceipt back of a clock exchanged with a paired storage system. In suchan example, each storage system may have its own synchronous replicationlease and its own cluster membership lease with every paired storagesystem. The expiration of a synchronous replication lease with any pairmay result in paused processing. Cluster membership, however, cannot berecalculated until the cluster membership lease has expired with allpairs. As such, the duration of the cluster membership lease should beset, based on the message and clock value interactions, to ensure thatthe cluster membership lease with a pair will not expire until after apair's synchronous replication link for that link has expired. Readerswill appreciate that a cluster membership lease can be established byeach storage system in a pod and may be associated with a communicationlink between any two storage systems that are members of the pod.Furthermore, the cluster membership lease may extend after theexpiration of the synchronous replication lease for a duration of timethat is at least as long as the time period for expiration of thesynchronous replication lease. The cluster membership lease may beextended on receipt of a clock value received from a paired storagesystem as part of a clock exchange, where the cluster membership leaseperiod from the current clock value may be at least as long as theperiod established for the last synchronous replication lease extensionbased on exchanged clock values. In additional embodiments, additionalcluster membership information can be exchanged over a connection,including when a session is first negotiated. Readers will appreciatethat in embodiments that utilize a cluster membership lease, eachstorage system (or storage controller) may have its own value for thecluster membership lease. Such a lease should not expire until it can beassured that all synchronous replication leases across all pod memberswill have expired given that the cluster lease expiration allowsestablishing new membership such as through a mediator race and thesynchronous replication lease expiration forces processing of newrequests to pause. In such an example, the pause must be assured to bein place everywhere before cluster membership actions can be taken.

Readers will appreciate that although only one of the storage systems(714) is depicted as identifying (702), for a dataset (712), a pluralityof storage systems (714, 724, 728) across which the dataset (712) willbe synchronously replicated, configuring (704) one or more datacommunications links (716, 718, 720) between each of the plurality ofstorage systems (714, 724, 728) to be used for synchronously replicatingthe dataset (712), exchanging (706), between the plurality of storagesystems (714, 724, 728), timing information (710, 722, 726) for at leastone of the plurality of storage systems (714, 724, 728), andestablishing (708), in dependence upon the timing information (710, 722,726) for at least one of the plurality of storage systems (714, 724,728), a synchronous replication lease, the remaining storage systems(724, 728) may also carry out such steps. In fact, all three storagesystems (714, 724, 728) may carry out one or more of the steps describedabove at the same time, as establishing a synchronous replicationrelationship between two or more storage systems (714, 724, 728) mayrequire collaboration and interaction between two or more storagesystems (714, 724, 728).

For further explanation, FIG. 8 sets forth a flow chart illustrating anadditional example method of establishing a synchronous replicationrelationship between two or more storage systems (714, 724, 728)according to some embodiments of the present disclosure. The examplemethod depicted in FIG. 8 is similar to the example method depicted inFIG. 7, as the example method depicted in FIG. 8 also includesidentifying (702), for a dataset (712), a plurality of storage systems(714, 724, 728) across which the dataset (712) will be synchronouslyreplicated, configuring (704) one or more data communications links(716, 718, 720) between each of the plurality of storage systems (714,724, 728) to be used for synchronously replicating the dataset (712),exchanging (706), between the plurality of storage systems (714, 724,728), timing information (710, 722, 726) for at least one of theplurality of storage systems (714, 724, 728), and establishing (708), independence upon the timing information (710, 722, 726) for at least oneof the plurality of storage systems (714, 724, 728), a synchronousreplication lease, the synchronous replication lease identifying aperiod of time during which the synchronous replication relationship isvalid.

In the example method depicted in FIG. 8, establishing (708), independence upon the timing information (710, 722, 726) for at least oneof the plurality of storage systems (714, 724, 728), a synchronousreplication lease can include coordinating (802) clocks between aplurality of storage systems (714, 724, 728). In the example methoddepicted in FIG. 8, coordinating (802) clocks between a plurality ofstorage systems (714, 724, 728) may be carried out, for example, throughthe exchange of one or more messages sent between the storage systems(714, 724, 728). The one or more messages sent between the storagesystems (714, 724, 728) may include information such as, for example,the clock value of a storage system whose clock value will be used byall other storage systems, an instruction for all storage systems to settheir clock values to a predetermined value, confirmation messages froma storage system that has updated its clock value, and so on. In such anexample, the storage systems (714, 724, 728) may be configured such thatthe clock value for a particular storage system (e.g., a leader storagesystem) should be used by all other storage systems, the clock valuefrom all of the storage system that meets some particular criteria(e.g., the highest clock value) should be used all other storagesystems, and so on. In such an example, some predetermined amount oftime may be added to a clock value received from another storage systemto account for transmission times associated with the exchange ofmessages.

In the example method depicted in FIG. 8, establishing (708), independence upon the timing information (710, 722, 726) for at least oneof the plurality of storage systems (714, 724, 728), a synchronousreplication lease can include exchanging (804) uncoordinated clocksbetween a plurality of storage systems (714, 724, 728). Exchanging (804)uncoordinated clocks between a plurality of storage systems (714, 724,728) may be carried, for example, by a storage controller in eachstorage system (714, 724, 728) exchanging values for a localmonotonically increasing clock as described in greater detail above. Insuch an example, each storage system (714, 724, 728) may utilize anagreed upon synchronous replication lease interval and messagingreceived from other storage systems (714, 724, 728) to establish (708) asynchronous replication lease.

The example method depicted in FIG. 8 also includes delaying (806) theprocessing of I/O requests received after the synchronous replicationlease has expired. I/O requests received by any of the storage systemsafter the synchronous replication lease has expired may be delayed(806), for example, for a predetermined amount of time that issufficient for attempting to re-establish a synchronous replicationrelationship, until a new synchronous replication lease has beenestablished, and so on. In such an example, a storage system may delay(806) the processing of I/O requests by failing with some type of ‘busy’or temporary failure indication, or in some other way.

For further explanation, FIG. 9 sets forth a flow chart illustrating anadditional example method of establishing a synchronous replicationrelationship between two or more storage systems (714, 724, 728)according to some embodiments of the present disclosure. The examplemethod depicted in FIG. 9 is similar to the example method depicted inFIG. 7, as the example method depicted in FIG. 9 also includesidentifying (702), for a dataset (712), a plurality of storage systems(714, 724, 728) across which the dataset (712) will be synchronouslyreplicated, configuring (704) one or more data communications links (716a, 716 b, 718 a, 718 b, 720 a, 720 b) between each of the plurality ofstorage systems (714, 724, 728) to be used for synchronously replicatingthe dataset (712), exchanging (706), between the plurality of storagesystems (714, 724, 728), timing information (710, 722, 726) for at leastone of the plurality of storage systems (714, 724, 728), andestablishing (708), in dependence upon the timing information (710, 722,726) for at least one of the plurality of storage systems (714, 724,728), a synchronous replication lease, the synchronous replication leaseidentifying a period of time during which the synchronous replicationrelationship is valid.

In the example method depicted in FIG. 9, configuring (704) one or moredata communications links (716 a, 716 b, 718 a, 718 b, 720 a, 720 b)between each of the plurality of storage systems (714, 724, 728) to beused for synchronously replicating the dataset (712) can includeconfiguring (902), for each of a plurality of data communications types,a data communications link (716 a, 716 b, 718 a, 718 b, 720 a, 720 b)between each of the plurality of storage systems (714, 724, 728) to beused for synchronously replicating the dataset (712). In the examplemethod depicted in FIG. 9, each storage system may be configured togenerate a plurality of data communications types that the storagesystem sends to other storage systems in a pod. For example, a storagesystem may generate data communications of a first type that includesdata that is part of I/O processing (e.g., data to be written to astorage system as part of a write request issued by a host), the storagesystem may be configured to generate data communications of a secondtype that includes configuration changes (e.g., information generated inresponse to create, extend, delete or rename volumes), the storagesystem may be configured to generate data communications of a third typethat includes communication involved in detecting and handling storagesystem and interconnect faults, and so on. In such an example, the datacommunications type may be determined, for example, based on whichsoftware module initiated the message, based on which hardware componentinitiated the message, based on the type of event that caused themessage to be initiated, and in other ways. In the example methoddepicted in FIG. 9, configuring (902) a data communications link (716 a,716 b, 718 a, 718 b, 720 a, 720 b) between each of the plurality ofstorage systems (714, 724, 728) for each of a plurality of datacommunications types may be carried out, for example, by configuring thestorage systems to use distinct interconnects for each of a plurality ofdata communications types, by configuring the storage systems to usedistinct networks for each of a plurality of data communications types,or in other ways.

The example method depicted in FIG. 9 also includes detecting (904) thatthe synchronous replication lease has expired. In the example methoddepicted in FIG. 9, detecting (904) that the synchronous replicationlease has expired may be carried out, for example, by a particularstorage system comparing a current clock value to the period of timeduring which the lease was valid. Consider an example in which thestorage systems (714, 724, 728) coordinated clocks to set the value of aclock within each storage system (714, 724, 728) to a value of 5000milliseconds and each storage system (714, 724, 728) was configured toestablish (708) a synchronous replication lease that extended for alease interval of 2000 milliseconds beyond their clock values, such thatthe synchronous replication lease for each storage system (714, 724,728) expired when the clock within a particular storage system (714,724, 728) reached a value that exceeds 7000 milliseconds. In such anexample, detecting (904) that the synchronous replication lease hasexpired may be carried out by determining that the clock within aparticular storage system (714, 724, 728) reached a value of 7001milliseconds or higher.

Readers will appreciate that the occurrence of other events may alsocause each storage system (714, 724, 728) to immediately treat asynchronous replication lease as being expired, For example, a storagesystem (714, 724, 728) may immediately treat a synchronous replicationlease as being expired upon detecting a communications failure betweenthe storage system (714, 724, 728) and another storage system (714, 724,728) in the pod, a storage system (714, 724, 728) may immediately treata synchronous replication lease as being expired upon receiving a leasere-establishment message from another storage system (714, 724, 728) inthe pod, a storage system (714, 724, 728) may immediately treat asynchronous replication lease as being expired upon detecting thatanother storage system (714, 724, 728) in the pod has failed, and so on.In such an example, the occurrence of any of the events described in thepreceding sentence may cause a storage system to detect (904) that thesynchronous replication lease has expired.

The example method depicted in FIG. 9 also includes re-establishing(906) a synchronous replication relationship. In the example methoddepicted in FIG. 9, re-establishing (906) a synchronous replicationrelationship may be carried out, for example, through the use of one ormore re-establishment messages. Such re-establishment messages caninclude, for example, an identification of a pod for which thesynchronous replication relationship is to be re-established,information needed to configure one or more data communications links,updated timing information, and so on. In such a way, the storagesystems (714, 724, 728) may re-establish (906) a synchronous replicationrelationship in much the same way that the synchronous replicationrelationship was initially created, including but not limited to, eachstorage system performing one or more of: identifying (702), for adataset (712), a plurality of storage systems (714, 724, 728) acrosswhich the dataset (712) will be synchronously replicated, configuring(704) one or more data communications links (716 a, 716 b, 718 a, 718 b,720 a, 720 b) between each of the plurality of storage systems (714,724, 728) to be used for synchronously replicating the dataset (712),exchanging (706), between the plurality of storage systems (714, 724,728), timing information (710, 722, 726) for at least one of theplurality of storage systems (714, 724, 728), and establishing (708), independence upon the timing information (710, 722, 726) for at least oneof the plurality of storage systems (714, 724, 728), a synchronousreplication lease, the synchronous replication lease identifying aperiod of time during which the synchronous replication relationship isvalid.

In the example method depicted in FIG. 9, the expiration of asynchronous replication lease may be followed by some set of events,followed by a reestablishment message, followed by a new activity lease,or followed by some other action. Data communications, configurationcommunications, or other communications might be in transit while thesynchronous replication lease expires and is re-established. In fact,communication may not be received, for example, until after a newsynchronous replication lease has been established. In such cases, thecommunications may have been sent based on one understanding of the pod,cluster, or network link state, and may be received by a storage system(714, 724, 728) that now has a different understanding of one or anotheraspect of that state. As such, there should generally be some means ofensuring that received communications are discarded if thecommunications were sent prior to some set of cluster or link statechanges. There are several possible ways of ensuring that receivedcommunications are discarded if the communications were sent prior tosome set of cluster or link state changes. One way of ensuring thatreceived communications are discarded if the communications were sentprior to some set of cluster or link state changes is to establish somesession identifier (e.g., a number) that is associated with establishingor reestablishing a link with a working synchronous replication leasethat is being extended. After a cluster communications link isreestablished, the link gets a new session identifier. This identifiercan be included with data, configuration, or other communicationmessages. Any message that is received with the wrong session identifieris discarded or results in an error response indicating a mismatchedsession identifier.

Readers will appreciate that the manner in which storage systems (714,724, 728) respond to the re-establishment of a synchronous replicationlease may change based on different embodiments that the storage systemsand the pods may take. In the case of simple primary controllers withtwo storage systems, any new request to perform an operation on astorage system (reads, writes, file operations, object operations,administrative operations, etc.) that is received after the receivingcontroller's synchronous replication lease has expired may have itsprocessing delayed, dropped, or failed with some kind of “retry later”error code. As such, a running primary storage controller can be assuredthat the paired storage controller is not processing new requests if itcan be assured that the paired storage controller's synchronousreplication lease has expired, which it can be assured of when its ownreestablishment lease has expired. After the reestablishment lease hasexpired it is safe for the controller to start looking further atcorrective actions, including considering the paired controller to beoffline and then continuing storage processing without the pairedcontroller. Exactly what actions those might be can differ based on awide variety of considerations and implementation details.

In the case of storage systems with primary and secondary controllers, astill running primary controller on one storage system might try toconnect to the paired storage system's previous secondary controller, onthe presumption that the paired storage system's previous secondarycontroller might be taking over. Or, a still running primary controlleron one storage system might wait for some particular amount of time thatis the likely maximum secondary takeover time. If the secondarycontroller connects and establishes a new connection with a newsynchronous replication lease within a reasonable time, then the pod maythen recover itself to a consistent state (described later) and thencontinue normally. If the paired secondary controller doesn't connectquickly enough, then the still running primary controller may takefurther action, such as trying to determine whether the still runningprimary controller should consider the paired storage system to befaulted and then continue operating without the paired storage system.Primary controllers might instead keep active, leased, connections tosecondary controllers on paired storage systems within a pod. In thatcase, expiration of the primary-to-primary reestablishment lease mightresult instead in a surviving primary using that connection to query forsecondary takeover, rather than there being a need to establish thatconnection in the first place. It is also possible that two primarystorage controllers are running, while the network isn't working betweenthem, but the network is working between one or the other primarycontroller and the paired secondary controller. In that case, internalhigh availability monitoring within the storage system might not detecta condition on its own that triggers a failover from primary tosecondary controller. Responses to that condition include: triggering afailover from primary to secondary anyway, just to resume synchronousreplication, routing communication traffic from a primary through asecondary, or operating exactly as if communication had failedcompletely between the two storage systems, resulting in the same faulthandling as if that had happened.

If multiple controllers are active for a pod (including in both dualactive-active controller storage systems and in scale-out storagesystems), leases might still be kept by individual controller clustercommunications with any or all controllers in a paired storage system.In this case, an expired synchronous replication lease might need toresult in pausing of new request processing for a pod across the entirestorage system. The lease model can be extended with exchanging ofclocks and paired clock responses between all active controllers in astorage system, with the further exchanging of those clocks with anypaired controllers in the paired storage systems. If there is anoperating path over which a particular local controller's clock isexchanged with any paired controller, then the controller can use thatpath for an independent synchronous replication lease and possibly foran independent reestablishment lease. In this case, local controllerswithin a storage system may be further exchanging clocks between eachother for local leases between each other as well. This may already beincorporated into the local storage system's high availability andmonitoring mechanisms, but any timings related to the storage system'shigh availability mechanisms should be taken into account in theduration of the activity and reestablishment leases, or in any furtherdelays between reestablishment lease expiration and actions taken tohandle an interconnect fault.

Alternately, storage-system-to-storage-system cluster communications orlease protocols alone may be assigned to one primary controller at atime within an individual multi-controller or scale-out storage system,at least for a particular pod. This service may migrate from controllerto controller as a result of faults or, perhaps, as a result of loadimbalances. Or cluster communications or lease protocols might run on asubset of controllers (for example, two) in order to limit clockexchanges or the complexity of analyzing fault scenarios. Each localcontroller may need to exchange clocks within the controllers thathandle storage system to storage system leases, and the time to respondafter a lease expiration might have to be adjusted accordingly, toaccount for potential cascading delays in when individual controllerscan be ensured to have effected a processing pause. Connections that arenot currently depended on for leases related to processing pausing mightstill be monitored for alerting purposes.

The example method depicted in FIG. 9 also includes attempting (908) totake over I/O processing for the dataset. In the example method depictedin FIG. 9, attempting (908) to take over I/O processing for the dataset(712) may be carried, for example, by a storage system (714, 724, 728)racing to a mediator. If a particular storage system (714, 724, 728)successfully takes over I/O processing for the dataset (712), allaccesses of the dataset (712) will be serviced by the particular storagesystem (714, 724, 728) until a synchronous replication relationship canbe reestablished and any changes to the dataset (712) that occurredafter the previous synchronous replication relationship expired can thenbe transferred and persisted on the other storage systems (714, 724,728). In such an example, an attempt (908) to take over I/O processingfor the dataset (712) may only occur after the expiration of some periodof time after the synchronous replication lease expires. For example,attempts to resolve how to proceed after link failure (including one ormore of the storage systems attempting to take over I/O processing forthe dataset) may not start until a time period after the synchronousreplication lease has expired that is, for example, at least as long asthe maximum lease time resulting from clock exchanges.

Readers will appreciate that in many of the examples depicted above,although only one of the storage systems (714) is depicted as carryingout the steps described above, in fact, all storage systems (714, 724,728) in a pod (or in a pod that is being formed) may carry out one ormore of the steps described above at the same time, as establishing asynchronous replication relationship between two or more storage systemsmay require collaboration and interaction between two or more storagesystems.

When any one or more storage systems that are members of a pod areinterrupted, then any remaining storage systems, or any storage systemsthat resume operation earlier, may either detach them (so that they areno longer in-sync) or will wait for them and participate in a recoveryaction to ensure consistency before moving forward. If the outage isshort enough, and recovery is quick enough, then operating systems andapplications external to the storage systems, or running on a storagesystem that does not fault in a way that brings the application itselfdown, may experience a temporary delay in storage operation processingbut may not experience a service outage. SCSI and other storageprotocols support retries, including to alternate target storageinterfaces, in the case of operations lost due to a temporary storagecontroller or interface target controller outage, and SCSI in particularsupports a BUSY status which requests initiator retries which could beused while a storage controller participates in recovery.

In general, one of the goals of recovery is to handle anyinconsistencies from an unexpected disruption of in-progress,distributed operations and to resolve the inconsistencies by makingin-sync pod member storage systems sufficiently identical. At thatpoint, providing the pod service can be safely resumed. Sufficientlyidentical at least includes the content stored in the pod, and in othercases, sufficiently identical may include the state of persistentreservations. Sufficiently identical may also include ensuring thatsnapshots are either consistent—and still correct with respect tocompleted, concurrent, or more recently received modifying operations—orconsistently deleted. Depending on an implementation, there may be othermetadata that should be made consistent. If there is metadata used fortracking or optimizing the transfer of content from a replication sourceto an asynchronous or snapshot-based replication target, then that mightneed to be made consistent to allow the replication source to switchseamlessly from one member storage system of a pod to another memberstorage system. The existence and properties of volumes may also need tobe recovered, and perhaps definitions related to applications orinitiating host systems. Many of these properties may be recovered usingstandard database transaction recovery techniques, depending on how theyare implemented.

In some examples, beyond ensuring that administrative metadata issufficiently identical in a storage system that implements modifyingoperations to content in a block-based storage system, recovery mustensure that that those modifications are applied or discardedconsistently across a pod and with proper consideration for blockstorage semantics (order, concurrency, consistency, atomicity foroperations such as COMPARE AND WRITE and XDWRITEREAD). At core, thisimplementation relies on being able to know during recovery whatoperations might have been applied to at least one in-sync storagesystem for a pod that might not have been applied to all other in-syncstorage systems for the pod, and either applying them everywhere orbacking them out. Either action results in consistency—apply everywhereor backout everywhere—and there is no inherent reason why the answer hasto be uniform across all operations. Backout may be allowed if at leastone in-sync storage system for the pod did not apply the operation. Ingeneral, it is often simpler to reason about applying all updates thatwere found on any in-sync storage system for a pod rather than backingout some or all updates that are on one or more in-sync storage systemsfor a pod but that are not on all in-sync storage systems for the pod.To be efficient, knowing what was applied on some systems that might nothave been applied on other systems generally requires that the storagesystems record something other than the raw data (otherwise, all datamight have to be compared which could be enormously time consuming).Discussed below is additional detail regarding implementations forrecording such information that may enable storage system recovery.

Two examples for persistently tracking information for ensuringconsistency include: (1) identifying that the content of volumes mightbe different across in-sync storage systems for the pod, and (2)identifying collections of operations that might not have beenuniversally applied across all in-sync storage systems for the pod. Thefirst example is a traditional model for mirroring: keep a tracking mapof logical regions that are being written (often as a list or as abitmap covering a volume's logical space with some granularity) and usethat list during recovery to note which regions might differ between onecopy and another. The tracking map is written to some or all mirrors (oris written separately) prior or during the write of the volume data insuch a way that recovery of the tracking map is guaranteed to cover anyvolume regions that were in flux at the time of a fault. Recovery inthis first variation generally consists of copying content from one copyto another to make sure they are the same.

The second example in persistent tracking—based on operationtracking—may be useful in storage systems that support synchronouslyreplicating virtual copying of large volume ranges within and betweenvolumes in a pod since this case can be more difficult or expensive totrack simply as potential differences in volume content betweensynchronously replicated storage systems (though see a later sectiondescribing tracking and recovery in content-addressable storagesystems). Also, simple content tracking might work less well in storagesystems where synchronous replication must track more complexinformation, such as in content tracking graphs with extent and largergranularity identifiers that drive forms of asynchronous replication andwhere the asynchronous replication source can be migrated or faultedover from one in-sync storage system in a pod to another. Whenoperations are tracked instead of content, recovery includes identifyingoperations that may not have completed everywhere. Once such operationshave been identified, any ordering consistency issues should beresolved, just as they should be during normal run-time using techniquessuch as leader-defined ordering or predicates or through interlockexceptions. An interlock exception is described below, and with regardto predicates, descriptions of relationships between operations andcommon metadata updates may be described as a set of interdependenciesbetween separate, modifying operations—where these interdependencies maybe described as a set of precursors that one operation depends on insome way, where the set of precursors may be considered predicates thatmust be true for an operation to complete. To continue with thisexample, given the identified operations, the operations may then bereapplied. Recorded information about operations should include anymetadata changes that should be consistent across pod member storagesystems, and this recorded information can then be copied and applied.Further, predicates, if they are used to disseminate restrictions onconcurrency between leaders and followers, might not need to bepreserved, if those predicates drive the order in which storage systemspersist information, since the persisted information implies the variousplausible outcomes.

As discussed more thoroughly within U.S. Provisional Patent ApplicationSer. Nos. 62/470,172 and 62/518,071, references that are incorporatedherein in their entirety, a set of in-sync storage systems may implementa symmetric I/O model for providing data consistency. In a symmetric I/Omodel, multiple storage systems may maintain a dataset within a pod, anda member storage system that receives an I/O operation may process theI/O operation locally concurrent with the processing of the I/Ooperation on all the other storage systems in the pod—where thereceiving storage system may initiate the processing of the I/Ooperation on the other storage systems. However, in some cases, multiplestorage systems may receive independent I/O operations that write tooverlapping memory regions. For example, if a first write comes in to afirst storage system, then the first storage system may begin persistingthe first write locally while also sending the first write to a secondstorage system—while at about the same time, a second write, to anoverlapping volume region with the first write, is received at a secondstorage system, where the second storage system begins persisting thesecond write locally while also sending the second write to the firststorage system. In this scenario, at some point, either the firststorage system, the second storage system, or both storage systems maynotice that there is a concurrent overlap. Further in this scenario, thefirst write can not be completed on the first storage system until boththe second storage system has persisted the first write and respondedwith a success indication, and the first storage system has successfullypersisted the first write—where the second storage system is in asimilar situation with the second write. Because both storage systemshave access to both the first and second writes, either storage systemmay detect the concurrent overlap, and when one storage system detectsthe concurrent overlap, the storage system may trigger an exception,which is referred to herein as an “interlock exception.” One solutionincludes the two, or possibly more storage systems when the scenario isexpanded to additional storage systems, storage systems involved in aninterlock exception to reach agreement on which write operationprevails.

In another example, such as in the case of overlapping write requests,write-type requests (e.g., WRITE, WRITE SAME, and UNMAP requests, orcombinations) that were overlapping in time and in volume address rangeat the time of an event that interrupted replication and led to aneventual recovery might have completed inconsistently between thein-sync storage systems. The manner in which this situation is handledcan depend on the implementation of the I/O path during normaloperation. In this example, discussed further below, is a first andsecond write that overlapped in time, where each was received by onestorage system or another for a pod before either was signaled as havingcompleted. This example is readily extended to more than two writes byconsidering each two in turn, and to more than two storage systems byconsidering that a first write and a second write might have completedon more than one storage system, and by considering that a first,second, and third write (or additional writes) might have completedinconsistently on three or more storage systems. The techniquesdescribed are easily extended to these cases. In a symmetric I/O -basedstorage system implementation based on interlock exceptions, only thefirst write might have completed on one storage system while only thesecond of the two overlapping writes might have completed on a secondstorage system. This case can be detected by noticing that the rangesoverlap between each write, and by noticing that neither storage systemincludes the alternate overlapping write. If the two writes overlapcompletely (one completely covers the other), then one of the two writesmay simply be copied to the other storage system and applied to replacethat storage system's content for that volume address range. If thewrites overlap only partially, then the content that partially overlapscan be copied from one storage system to the other (and applied), whilethe parts that don't overlap can be copied between each storage systemso that the content is made uniform and up-to-date on both storagesystems. In a leader based system with predicates or some other meansfor the leader to declare that one write precedes another, the storagesystems performing the writes may well persist one before the other, orpersist the two together. In another case, the implementation maypersist the two writes separately and out of order, with the orderingpredicates used merely to control completion signaling. If theimplementation allows out-of-order write processing, then the precedingexample explains how consistency can be recovered. In cases wherestorage systems enforce ordering of persistence during normal operation,then recovery might still see only the first write on a first storagesystem, but the first and second writes on a second storage system. Inthat case, the second write can be copied from the second storage systemto the first storage system as part of recovery.

In another example snapshots may also be recovered. In some cases, suchas for snapshots concurrent with modifications where a leader determinedsome modifications should be included in the snapshot and othersshouldn't, the recorded information might include information aboutwhether a particular write should be included within a snapshot or not.In that model, it may not be necessary to ensure that everything that aleader decided to include in a snapshot must end up included in thesnapshot after a recovery. If one in-sync storage system for a podrecorded the existence of the snapshot and no in-sync storage system forthe pod recorded a write that was ordered for inclusion in the snapshot,then uniformly applying the snapshot without including that write stillresults in snapshot content that is entirely consistent across allin-sync storage systems for the pod. This discrepancy should only occurin the case of concurrent writes and snapshots that had never beensignaled as completed so no inclusion guarantee is warranted: the leaderassigning predicates and ordering may be necessary only for run-timeconsistency rather than for recovery order consistency. In cases whererecovery identifies a write for inclusion in a snapshot, but whererecovery doesn't locate the write, the snapshot operation itself mightsafely ignore the snapshot depending on the implementation. The sameargument about snapshots applies to virtual copying of a volume addressrange through SCSI EXTENDED COPY and similar operations: the leaderdefines which writes to the source address range might logically precedethe copy and which writes to the target address range might logicallyprecede or follow the address range copy. However, during recovery, thesame arguments apply as with snapshots: a concurrent write with a volumerange copy could miss either the concurrent write or the volume rangecopy as long as the result is consistent across in-sync storage systemsfor a pod and does not roll back a modification that had completedeverywhere and does not reverse a modification that a dataset consumermight have read.

Further with regard to this example describing recovery of snapshots, ifany storage system applied the write for a COMPARE AND WRITE, then thecomparison must have succeeded on one in-sync storage system for a pod,and run-time consistency should have meant that the comparison shouldhave succeeded on all in-sync storage systems for the pod, so if anysuch storage system had applied the write, it can be copied and appliedto any other in-sync storage system for the pod that had not applied itprior to recovery. Further still, recovery of XDWRITEREAD or XPWRITErequests (or similar arithmetic transformation operations betweenpre-existing data and new data) could operate either by delivering theresult of the transformation after reading that result from one storagesystem, or it can operate by delivering the operation with thetransforming data to other storage systems if it can be ensured that anyordering data preceding the transforming write is consistent acrossin-sync storage systems for the pod and if it can be reliably determinedwhich such storage systems had not yet applied the transforming write.

As another example, recovery of metadata may be implemented. In thiscase, recovery should also result in consistent recovery of metadatabetween in-sync storage system for a pod, where that metadata isexpected to be consistent across the pod. As long as this metadata isincluded with operations, these can be applied along with contentupdates described by those operations. The manner in which this data ismerged with existing metadata depends on the metadata and theimplementation. Longer-term change tracking information for drivingasynchronous replication can often be merged quite simply as nearby orotherwise related modifications are identified.

As another example, recording recent activity for operation tracking maybe implemented in various ways to identify operations that were inprogress on in-sync storage systems in a pod at the time of a fault orother type of service interruption that led to a recovery. For example,one model is to record recovery information in modifications to eachin-sync storage system within a pod either atomically with anymodification (which can work well if the updates are staged through fastjournaling devices) or by recording information about operations thatwill be in progress before they can occur. The recorded recoveryinformation may include a logical operation identifier, such as based onthe original request or based on some identifier assigned by a leader aspart of describing the operation, and whatever level of operationdescription may be necessary for recovery to operate. Informationrecorded by a storage system for a write which is to be included in thecontent of a concurrent snapshot should indicate that the write is to beincluded in the snapshot as well as in the content of the volume thatthe write is applied to. In some storage system implementations, thecontent of a snapshot is automatically included in the content of thevolume unless replaced by specific overlapping content in a newersnapshot or replaced by specific overlapping content written later tothe live the volume. Two concurrent write-type requests (e.g., WRITE,WRITE SAME or UNMAP requests, or combinations) which overlap in time andin volume address may be explicitly ordered by a leader such that theleader ensures that the first write is persisted first to all in-syncstorage systems for a pod before the second one can be persisted by anyin-sync storage system for the pod. This ensures, in a simple way, thatinconsistencies cannot happen. Further, since concurrent overlappingwrites to a volume are very rare, this may be acceptable. In that case,if there is a record on any recovering storage system for the secondwrite, then the first write must have completed everywhere so it shouldnot need recovery. Alternately, a predicate may be described by theleader requiring that storage systems order a first write before asecond write. The storage systems may then perform both writes together,such that they are guaranteed to either both persist or both fail topersist. In another case, the storage system may persist the first writeand then the second write after the persistence of the first write isassured. A COMPARE AND WRITE, XDWRITEREAD, or XPWRITE request should beordered in such a way that the precursor content is identical on allstorage systems at the time each performs the operation. Alternately,one storage system might calculate the result and deliver the request toall storage systems as a regular write-type request. Further, withregard to making these operations recoverable, tracking which operationshave completed everywhere may allow their recency to be discounted andrecorded information that causes an operation recovery analysis forcompleted operations can then be either discarded or efficiently skippedover by recovery.

In another example, clearing out completed operations may beimplemented. One example to handle clearing of recorded information isto clear it across all storage systems after the operation is known tohave been processed on all in-sync storage systems for the pod. This canbe implemented by having the storage system which received the requestand which signaled completion send a message to all storage systems forthe pod after completion is signaled, allowing each storage system toclear them out. Recovery then involves querying for all recordedoperations that have not been cleared out across all in-sync storagesystems for the pod that are involved in the recovery. Alternately,these messages could be batched so that they happen periodically (e.g.,every 50 ms), or after some number of operations (say, every 10 to 100).This batching process may reduce message traffic significantly at theexpense of somewhat increased recovery times since more fully completedoperations are reported as potentially incomplete. Further, in a leaderbased implementation (as an example), the leader could be made aware ofwhich operations are completed and it could send out the clear messages.

In another example, a sliding window may be implemented. Such an examplemay work well in implementations based on leaders and followers, wherethe leader may attach a sequence number to operations or collections ofoperations. In this way, in response to the leader determining that alloperations up to some sequence number have completed, it may send amessage to all in-sync storage systems indicating that all operations upto that sequence number have completed. The sequence number could alsobe an arbitrary number, such that when all operations associated with anarbitrary number have completed, a message is sent to indicate all thoseoperations have completed. With a sequence number based model, recoverycould query for all operations on any in-sync storage system associatedwith a sequence number larger than the last completed sequence number.In a symmetric implementation without a leader, each storage system thatreceives request for the pod could define its own sliding window andsliding window identity space. In that case, recovery may includequerying for all operations on any in-sync storage window that areassociated with any sliding window identity space whose sliding windowidentity is after the last identity which has completed where operationsfor all preceding identifiers have also completed.

In another example, checkpoints may be implemented. In a checkpointmodel, special operations may be inserted by a leader which depend onthe completion of a uniform set of precursor operations and that allsuccessive operations then depend on. Each storage system may thenpersist the checkpoint in response to all precursor operations havingbeen persisted or completed. A successive checkpoint may be startedsometime after the previous checkpoint has been signaled as persisted onall in-sync storage systems for the pod. A successive checkpoint wouldthus not be initiated until some time after all precursor operations arepersisted across the pod; otherwise, the previous checkpoint would nothave completed. In this model, recovery may include querying for alloperations on all in in-sync storage systems that follow after theprevious to last checkpoint. This could be accomplished by identifyingthe second to last checkpoint known to any in-sync storage system forthe pod, or by asking each storage system to report all operations sinceits second to last persisted checkpoint. Alternately, recovery mayinclude searching for the last checkpoint known to have completed on allin-sync storage systems and may include querying for all operations thatfollow on any in-sync storage system—if a checkpoint completed on allin-sync storage systems, then all operations prior to that checkpointwere clearly persisted everywhere.

In another example, recovery of pods based on replicated directedacyclic graphs of logical extents may be implemented. However, prior todescribing such an implementation, storage systems using directedacyclic graphs of logical extents are first described.

A storage system may be implemented based on directed acyclic graphscomprising logical extents. In this model, logical extents can becategorized into two types: leaf logical extents, which reference someamount of stored data in some way, and composite logical extents, whichreference other leaf or composite logical extents.

A leaf extent can reference data in a variety of ways. It can pointdirectly to a single range of stored data (e.g., 64 kilobytes of data),or it can be a collection of references to stored data (e.g., a 1megabyte “range” of content that maps some number of virtual blocksassociated with the range to physically stored blocks). In the lattercase, these blocks may be referenced using some identity, and someblocks within the range of the extent may not be mapped to anything.Also, in that latter case, these block references need not be unique,allowing multiple mappings from virtual blocks within some number oflogical extents within and across some number of volumes to map to thesame physically stored blocks. Instead of stored block references, alogical extent could encode simple patterns: for example, a block whichis a string of identical bytes could simply encode that the block is arepeated pattern of identical bytes.

A composite logical extent can be a logical range of content with somevirtual size, which comprises a plurality of maps that each map from asubrange of the composite logical extent logical range of content to anunderlying leaf or composite logical extent. Transforming a requestrelated to content for a composite logical extent, then, involves takingthe content range for the request within the context of the compositelogical extent, determining which underlying leaf or composite logicalextents that request maps to, and transforming the request to apply toan appropriate range of content within those underlying leaf orcomposite logical extents.

Volumes, or files or other types of storage objects, can be described ascomposite logical extents. Thus, these presented storage objects (whichin most of our discussion will simply be referred to as volumes) can beorganized using this extent model.

Depending on implementation, leaf or composite logical extents could bereferenced from a plurality of other composite logical extents,effectively allowing inexpensive duplication of larger collections ofcontent within and across volumes. Thus, logical extents can be arrangedessentially within an acyclic graph of references, each ending in leaflogical extents. This can be used to make copies of volumes, to makesnapshots of volumes, or as part of supporting virtual range copieswithin and between volumes as part of EXTENDED COPY or similar types ofoperations.

An implementation may provide each logical extent with an identity whichcan be used to name it. This simplifies referencing, since thereferences within composite logical extents become lists comprisinglogical extent identities and a logical subrange corresponding to eachsuch logical extent identity. Within logical extents, each stored datablock reference may also be based on some identity used to name it.

To support these duplicated uses of extents, we can add a furthercapability: copy-on-write logical extents. When a modifying operationaffects a copy-on-write leaf or composite logical extent the logicalextent is copied, with the copy being a new reference and possiblyhaving a new identity (depending on implementation). The copy retainsall references or identities related to underlying leaf or compositelogical extents, but with whatever modifications result from themodifying operation. For example, a WRITE, WRITE SAME, XDWRITEREAD,XPWRITE, or COMPARE AND WRITE request may store new blocks in thestorage system (or use deduplication techniques to identify existingstored blocks), resulting in modifying the corresponding leaf logicalextents to reference or store identities to a new set of blocks,possibly replacing references and stored identities for a previous setof blocks. Alternately, an UNMAP request may modify a leaf logicalextent to remove one or more block references. In both types of cases, aleaf logical extent is modified. If the leaf logical extent iscopy-on-write, then a new leaf logical extent will be created that isformed by copying unaffected block references from the old extent andthen replacing or removing block references based on the modifyingoperation.

A composite logical extent that was used to locate the leaf logicalextent may then be modified to store the new leaf logical extentreference or identity associated with the copied and modified leaflogical extent as a replacement for the previous leaf logical extent. Ifthat composite logical extent is copy-on-write, then a new compositelogical extent is created as a new reference or with a new identity, andany unaffected references or identities to its underlying logicalextents are copied to that new composite logical extent, with theprevious leaf logical extent reference or identity being replaced withthe new leaf logical extent reference or identity.

This process continues further backward from referenced extent toreferencing composite extent, based on the search path through theacyclic graph used to process the modifying operation, with allcopy-on-write logical extents being copied, modified, and replaced.

These copied leaf and composite logical extents can then drop thecharacteristic of being copy on write, so that further modifications donot result in an additional copy. For example, the first time someunderlying logical extent within a copy-on-write “parent” compositeextent is modified, that underlying logical extent may be copied andmodified, with the copy having a new identity which is then written intoa copied and replaced instance of the parent composite logical extent.However, a second time some other underlying logical extent is copiedand modified and with that other underlying logical extent copy's newidentity being written to the parent composite logical extent, theparent can then be modified in place with no further copy and replacenecessary on behalf of references to the parent composite logicalextent.

Modifying operations to new regions of a volume or of a compositelogical extent for which there is no current leaf logical extent maycreate a new leaf logical extent to store the results of thosemodifications. If that new logical extent is to be referenced from anexisting copy-on-write composite logical extent, then that existingcopy-on-write composite logical extent will be modified to reference thenew logical extent, resulting in another copy, modify, and replacesequence of operations similar to the sequence for modifying an existingleaf logical extent.

If a parent composite logical extent cannot be grown large enough (basedon implementation) to cover an address range associated that includesnew leaf logical extents to create for a new modifying operation, thenthe parent composite logical extent may be copied into two or more newcomposite logical extents which are then referenced from a single“grandparent” composite logical extent which yet again is a newreference or a new identity. If that grandparent logical extent isitself found through another composite logical extent that iscopy-on-write, then that another composite logical extent will be copiedand modified and replaced in a similar way as described in previousparagraphs. This copy-on-write model can be used as part of implementingsnapshots, volume copies, and virtual volume address range copies withina storage system implementation based on these directed acyclic graphsof logical extents. To make a snapshot as a read-only copy of anotherwise writable volume, a graph of logical extents associated withthe volume is marked copy-on-write and a reference to the originalcomposite logical extents are retained by the snapshot. Modifyingoperations to the volume will then make logical extent copies as needed,resulting in the volume storing the results of those modifyingoperations and the snapshots retaining the original content. Volumecopies are similar, except that both the original volume and the copiedvolume can modify content resulting in their own copied logical extentgraphs and subgraphs.

Virtual volume address range copies can operate either by copying blockreferences within and between leaf logical extents (which does notitself involve using copy-on-write techniques unless changes to blockreferences modifies copy-on-write leaf logical extents). Alternately,virtual volume address range copies can duplicate references to leaf orcomposite logical extents, which works well for volume address rangecopies of larger address ranges. Further, this allows graphs to becomedirected acyclic graphs of references rather than merely referencetrees. Copy-on-write techniques associated with duplicated logicalextent references can be used to ensure that modifying operations to thesource or target of a virtual address range copy will result in thecreation of new logical extents to store those modifications withoutaffecting the target or the source that share the same logical extentimmediately after the volume address range copy operation.

Input/output operations for pods may also be implemented based onreplicating directed acyclic graphs of logical extents. For example,each storage system within a pod could implement private graphs oflogical extents, such that the graphs on one storage system for a podhave no particular relationship to the graphs on any second storagesystem for the pod. However, there is value in synchronizing the graphsbetween storage systems in a pod. This can be useful forresynchronization and for coordinating features such as asynchronous orsnapshot based replication to remote storage systems. Further, it may beuseful for reducing some overhead for handling the distribution ofsnapshot and copy related processing. In such a model, keeping thecontent of a pod in sync across all in-sync storage systems for a pod isessentially the same as keeping graphs of leaf and composite logicalextents in sync for all volumes across all in-sync storage systems forthe pod, and ensuring that the content of all logical extents isin-sync. To be in sync, matching leaf and composite logical extentsshould either have the same identity or should have mappable identities.Mapping could involve some set of intermediate mapping tables or couldinvolve some other type of identity translation. In some cases,identities of blocks mapped by leaf logical extents could also be keptin sync.

In a pod implementation based on a leader and followers, with a singleleader for each pod, the leader can be in charge of determining anychanges to the logical extent graphs. If a new leaf or composite logicalextent is to be created, it can be given an identity. If an existingleaf or composite logical extent is to be copied to form a new logicalextent with modifications, the new logical extent can be described as acopy of a previous logical extent with some set of modifications. If anexisting logical extent is to be split, the split can be described alongwith the new resulting identities. If a logical extent is to bereferenced as an underlying logical extent from some additionalcomposite logical extent, that reference can be described as a change tothe composite logical extent to reference that underlying logicalextent.

Modifying operations in a pod thus comprises distributing descriptionsof modifications to logical extent graphs (where new logical extents arecreated to extend content or where logical extents are copied, modified,and replaced to handle copy-on-write states related to snapshots, volumecopies, and volume address range copies) and distributing descriptionsand content for modifications to the content of leaf logical extents. Anadditional benefit that comes from using metadata in the form ofdirected acyclic graphs, as described above, is that I/O operations thatmodify stored data in physical storage may be given effect at a userlevel through the modification of metadata corresponding to the storeddata in physical storage—without modifying the stored data in physicalstorage. In the disclosed embodiments of storage systems, where thephysical storage may be a solid state drive, the wear that accompaniesmodifications to flash memory may be avoided or reduced due to I/Ooperations being given effect through the modifications of the metadatarepresenting the data targeted by the I/O operations instead of throughthe reading, erasing, or writing of flash memory. Further, invirtualized storage systems, the metadata described above may be used tohandle the relationship between virtual, or logical, addresses andphysical, or real, addresses—in other words, the metadata representationof stored data enables a virtualized storage system that may beconsidered flash-friendly in that it reduces, or minimizes, wear onflash memory.

Leader storage systems may perform their own local operations toimplement these descriptions in the context of their local copy of thepod dataset and the local storage system's metadata. Further, thein-sync followers perform their own separate local operations toimplement these descriptions in the context of their separate local copyof the pod dataset and their separate local storage system's metadata.When both leader and follower operations are complete, the result iscompatible graphs of logical extents with compatible leaf logical extentcontent. These graphs of logical extents then become a type of “commonmetadata” as described in previous examples. This common metadata can bedescribed as dependencies between modifying operations and requiredcommon metadata. Transformations to graphs can be described as separateoperations with a queue predicate relationship with subsequent modifyingoperations. Alternately, each modifying operation that relies on aparticular same graph transformation that has not yet been known tocomplete across the pod can include the parts of any graphtransformation that it relies on. Processing an operation descriptionthat identifies a “new” leaf or composite logical extent that alreadyexists can avoid creating the new logical extent since that part wasalready handled in the processing of some earlier operation, and caninstead implement only the parts of the operation processing that changethe content of leaf or composite logical extents. It is a role of theleader to ensure that transformations are compatible with each other.For example, we can start with two writes come that come in for a pod. Afirst write replaces a composite logical extent A with a copy of formedas composite logical extent B, replaces a leaf logical extent C with acopy as leaf logical extent D and with modifications to store thecontent for the second write, and further writes leaf logical extent Dinto composite logical extent B. Meanwhile, a second write implies thesame copy and replacement of composite logical extent A with compositelogical extent B but copies and replaces a different leaf logical extentE with a logical extent F which is modified to store the content of thesecond write, and further writes logical extent F into logical extent B.In that case, the description for the first write can include thereplacement of A with B and C with D and the writing of D into compositelogical extent B and the writing of the content of the first write intoleaf extend B; and, the description of the second write can include thereplacement of A with B and E with F and the writing of F into compositelogical extent B, along with the content of the second write which willbe written to leaf extent F. A leader or any follower can thenseparately process the first write or the second write in any order, andthe end result is B copying and replacing A, D copying and replacing C,F copying replacing E, and D and F being written into composite logicalextent B. A second copy of A to form B can be avoided by recognizingthat B already exists. In this way, a leader can ensure that the podmaintains compatible common metadata for a logical extent graph acrossin-sync storage systems for a pod.

Given an implementation of storage systems using directed acyclic graphsof logical extents, recovery of pods based on replicated directedacyclic graphs of logical extents may be implemented. Specifically, inthis example, recovery in pods may be based on replicated extent graphsthen involves recovering consistency of these graphs as well asrecovering content of leaf logical extents. In this implementation ofrecovery, operations may include querying for graph transformations thatare not known to have completed on all in-sync storage systems for apod, as well as all leaf logical extent content modifications that arenot known to have completed across all storage systems for the pod. Suchquerying could be based on operations since some coordinated checkpoint,or could simply be operations not known to have completed where eachstorage system keeps a list of operations during normal operation thathave not yet been signaled as completed. In this example, graphtransformations are straightforward: a graph transformation may createnew things, copy old things to new things, and copy old things into twoor more split new things, or they modify composite extents to modifytheir references to other extents. Any stored operation descriptionfound on any in-sync storage system that creates or replaces any logicalextent can be copied and performed on any other storage system that doesnot yet have that logical extent. Operations that describe modificationsto leaf or composite logical extents can apply those modifications toany in-sync storage system that had not yet applied them, as long as theinvolved leaf or composite logical extents have been recovered properly.

Further in this example, recovery of a pod may include the following:

-   -   querying all in-sync storage systems for leaf and composite        logical extent creations and their precursor leaf and composite        logical extents if any that were not known to have completed on        all in-sync storage systems for the pod;    -   querying all in-sync storage systems for modifying operations to        leaf logical extents that were not known to have completed on        all in-sync storage systems for the pod;    -   querying for logical address range copy operations as new        references to pre-existing leaf and composite logical extents;    -   identifying modifications that are not known to have completed        to leaf logical extents and where that leaf logical extent is        the source for a replacement leaf logical extent that also may        need recovery—so that modifications can be completed to that        leaf logical extent to all in-sync storage systems before the        leaf logical extent copy is recovered on any in-sync storage        systems that had not already copied it;    -   completing all leaf and composite logical extent copy        operations;    -   applying all further updates to leaf and composite logical        extents including naming new logical extent references, updating        leaf logical extent content, or removing logical extent        references; and    -   determining that all necessary actions have completed, at which        point further aspects of recovery can proceed.

In another example, as an alternative to using a logical extent graph,storage may be implemented based on a replicated content-addressablestore. In a content-addressable store, for each block of data (forexample, every 512 bytes, 4096 bytes, 8192 bytes or even 16384 bytes) aunique hash value (sometimes also called a fingerprint) is calculated,based on the block content, so that a volume or an extent range of avolume can be described as a list of references to blocks that have aparticular hash value. In a synchronously replicated storage systemimplementation based on references to blocks with the same hash value,replication could involve a first storage system receiving blocks,calculating fingerprints for those blocks, identifying block referencesfor those fingerprints, and delivering changes to one or a plurality ofadditional storage systems as updates to the mapping of volume blocks toreferenced blocks. If a block is found to have already been stored bythe first storage system, that storage system can use its reference toname the reference in each of the additional storage systems (eitherbecause the reference uses the same hash value or because an identifierfor the reference is either identical or can be mapped readily).Alternately, if a block is not found by the first storage system, thencontent of the first storage system may be delivered to other storagesystems as part of the operation description along with the hash valueor identity associated with that block content. Further, each in-syncstorage system's volume descriptions are then updated with the new blockreferences. Recovery in such a store may then include comparing recentlyupdated block references for a volume. If block references differbetween different in-sync storage systems for a pod, then one version ofeach reference can be copied to other storage systems to make themconsistent. If the block reference on one system does not exist, then itbe copied from some storage system that does store a block for thatreference. Virtual copy operations can be supported in such a block orhash reference store by copying the references as part of implementingthe virtual copy operation.

Additional details for implementing storage systems that synchronouslyreplicate a dataset may be found within U.S. Provisional ApplicationNos. 62/470,172 and 62/518,071, which are included by reference in theirentirety.

Initial synchronization of a storage system added to a pod—or subsequentresynchronization of a storage system that had been detached from apod—includes copying all content, or all missing content, from anin-sync storage system for a pod to an uninitialized, or out-of-sync,storage system prior to that storage system being brought online foractive use in providing the pod service. Such an initial synchronizationmay be performed for each storage system introduced as an extension of apod.

A difference between initial synchronization of content to a storagesystem added to a pod versus resynchronizing a storage system that had,through some set of events, become out-of-sync relative to the in-syncstorage systems for a pod, are conceptually quite similar. In the caseof a resynchronization, for example, all blocks that may differ betweenthe in-sync pod member storage systems and the out-of-sync pod memberare made up-to-date before the out-of-sync pod member can come backonline as an in-sync pod member storage system for the pod. In aninitial synchronization, this may include updating all blocks, andconsequently, it is conceptually similar to resynchronization where allblocks may differ. In other words, initial synchronization may beconsidered equivalent to reattaching a storage system that was detachedat the beginning of a pod, prior to any volume having been modified froman initial state, or prior to any volume having been created or added tothe pod.

Generally, resynchronization accomplishes at least two things to bring adetached pod back to a point where it is in-sync and can be brought backonline: (a) backing out, overwriting or otherwise replacing, any changesthat were persisted on the detached pod around the time it was detachedthat had not been retained by the in-sync pod members, and (b) updatingthe attaching storage system to match content and common metadata forthe pod. To be brought back online, a reattachment of a storage systemmay include re-enabling synchronous replication, re-enabling symmetricsynchronous replication, and re-enabling the receiving and processing ofoperations for the pod on the reattached storage system. Operations forthe pod may include reads, data modification operations, oradministrative operations.

In the process of detaching a storage system, some number of operationsmay have been in progress for the pod. Further, some of those operationsmay have persisted only on the detached storage system, other operationsmay have persisted only on the storage systems that remained in-syncimmediately after the detachment was processed, and other operations mayhave persisted on both the detached storage system and the storagesystems that remained in-sync. In this example, because the in-syncstate for the pod could not have recorded the operations persisted onlyon the detached storage system, any updates to the in-sync content andcommon metadata for the pod since the detachment of the storage systemwould not include those updates, which is the reason these updatesshould be backed out—either explicitly by undoing the updates, orimplicitly by overwriting that content as part of a resynchronizationprocess. On the in-sync storage systems themselves, there may be twolists to be accounted for prior to starting a reattachment of a detachedstorage system: (a) a list of operations, which may be referred to as anin-sync pending operations list at detach, that were in progress andwere persisted on any storage system that was in-sync when thereattaching storage system was detached from a pod and that remainedin-sync for any duration of time after the detach from the pod, and (b)a list of changes to content or common metadata during the window oftime the reattaching storage system was detached from the pod. Further,depending on the pod and storage system implementations, the two listsassociated with the in-sync storage systems may be represented by asingle list: content not known to be on the reattaching storage system.In a pod where multiple storage systems are detached, and in particularwhere those storage systems at different times, tracking of changessince each detach may yield separate lists—and how those lists aredescribed may vary considerably from one pod implementation to another.In some cases, an additional issue beyond tracking changes from the timeof detach and copying those changes to the attaching storage system isensuring that new modifying operations received during theresynchronization are applied to the attaching storage system.Conceptually, this problem may be described as ensuring that operationsto copy data and processing of modifying operations received by the podmay be merged in such a way that the result is correctly up-to-date atthe end of the attach and prior to considering the attaching storagesystem to be in-sync for the pod.

With respect to simple changed content resynchronization, one model forresynchronization is to generate a complete list of blocks—a detachedblock list—that may differ between the in-sync storage systems and theattaching storage system, and to start replicating any modifyingoperations as they would happen for a follower storage system. Acomplete list of blocks that may differ may include those from thein-sync pending operations list at detach from the in-sync storagesystems, the pending operations at the time of detach from the attachingstorage system, and blocks that were known to have changed since thedetach. Modifying operations may store their modifying content asdescribed, and resynchronization may proceed by locating ranges ofblocks from the detached block list and copying those blocks, insections, from an in-sync storage system to the attaching storagesystem. In this example, while copying a particular section, incomingmodifying operations that overlap with the section being copied may beheld off during the copy, or an arrangement may be made to apply thosemodifying operations after the section has been copied. This solutionmay create a problem for virtual block range copy operations, such asvirtualized implementations of the EXTENDED COPY operation. Further, thesource range for the copy may not yet be resynchronized, yet the targetrange may have already been resynchronized, which means that astraightforward implementation of the virtual block range copy operationmay (depending on the implementation) fail to either copy the correctdata to the target range because the data is not known at the time thevirtual block range copy operation is received, or may fail toresynchronize the target range correctly because the resynchronizeoperation itself may have presumed the target range was correctlysynchronized when it was never resynchronized in its final form.However, there are several solutions for this problem. One solution isto disallow virtual block range copy operations duringresynchronization. This may work in many cases because common uses ofvirtual block range copy operations—including client operating filesystems file copy operations and virtual machine clone or migrationoperations—typically respond to virtual block range copy failures bycopying content directly themselves through sequences of read and writerequests. Another solution is to remember incomplete virtual range copyoperations, not modifying operations that overwrite the target addressrange of any virtual address range copy operation, and then perform thecopy operation while accounting for overwrites when the source databecomes available. Given that the target of a resynchronization may notknow that the source data for a copy is not correct, all such operationsmay have to be deferred until the entire copy completes. Optimizationsare possible where the target of a resynchronization is made aware ofwhich regions have not yet been copied, or may be aware of when aresynchronization has completed processing a particular region of avolume.

Another aspect of resynchronizing storage systems may be updated blocktracking. For example, keeping a list of all individual blocks that aremodified while a storage system was detached (and then resynchronizingthem individually) may be impractical in some cases because an extendedoutage may result in a large number of blocks—and some storage systemscannot read large collections of non-sequential blocks very efficiently.Consequently, in some cases, it may be more practical to begin trackingregions, for example 1 MB ranges of a volume, to reduce the amount oftracked metadata. This course-grained tracking may be updated behindshorter term operation tracking, and may be preserved for as long as isneeded to handle a resynchronization of any out-of-date storage system,whether down for minutes, hours, days, or weeks. With solid statestorage, as opposed to mechanical spinning storage, tracking whichindividual blocks of a volume, or of a collection of volumes or anentire pod, may be quite practical, as is resynchronizing only thoseindividual blocks that have changed. Generally, there is very littlerandom read and write penalty, and there is little penalty to readingfrom a multi-level map, and consequently, it is relatively easy to mergefine-grained activity as operations over short time periods (forexample, in the 100 millisecond to 10 second range or every few hundredto every few thousand operations), into a fine-grained map naming allmodified blocks. Further, a list of recent activity may be a list thatcovers content modifications that have been recorded recently intojournaling devices (fast write storage such as various flavors of NVRAMintended to support high write bandwidth and a high overwrite rate), butwith metadata about those modifications perhaps preserved in journalsfor longer time periods than the actual content. In this example, amerged list of all activity may be a bitmap where each bit represents ablock or a small group of blocks, or it may be a list of block numbersor lists of block ranges organized by volume into a tree structure, suchas a B-tree. Such lists of block numbers may be compacted easily becausenearby block numbers may be stored as differences from one block numberto another block number.

Resynchronizing storage systems may also include block tracking bytracking sequence numbers. For example, some storage systems may, duringnormal operation, associate a respective sequence number with arespective modification—for all modifications. In such cases, the lastsequence number known to have been synchronized with a storage systemdetached from a pod may be all that is needed to query an in-syncstorage system for the pod to find all content that has been modifiedsince the detach, including any content that might not have beenreplicated to the detached array round the time of the detach.

Resynchronizing storage systems may also include tracking changes as asnapshot. For example, snapshots may be used to track changes since sometime in the past, and a storage system may manufacture a snapshot at thetime of a detach by excluding content that is not known to havecompleted. Alternatively, snapshots may be created on a regular basis,or with some periodicity, where the time of the snapshot creation may becompared to a time of a detach to determine which snapshot may serve asa basis for resynchronizing the detached storage system. As a variation,any snapshot created across a pod prior to a detach should be present inboth the in-sync and detached storage systems for the pod and may beused in various ways for resynchronization. For example, the content ofa storage system that is being reattached may be reverted back to itslast synchronized snapshot that predates the detach, and then rolledforward from that point to match current in-sync content in the pod.Generally, snapshots indicate a difference relative to a previoussnapshot or indicate a difference to current content. Using thesefeatures of snapshots, resynchronizing content to a reattaching storagesystem may include replicating differences between the time of thereattach and the time of the last complete pre-detach synchronizedsnapshot. In some cases, resynchronization may use a snapshot-basedmodel as a fallback. For example, short outages (such as outages ofabout a few minutes) may be handled through fine-grain tracking orrecording and replaying operations that have occurred since the time astorage system detached, and longer outages may be handled by revertingto snapshots taken every few minutes—where the threshold number ofminutes may be a default value or specified by a user or anadministrator. Such a configuration may be practical because relativelyinfrequent snapshots may have low long-term overhead but may generatemore data to be resynchronized. For example, a ten second outage may behandled through replaying recorded operations, where resynchronizationmay occur in ten seconds or less—whereas a snapshot taken five minutesprior to a detach may, in some cases, transfer up to five minutes' worthof content modification. In other cases, resynchronization after anoutage may be based on accumulated changes, such as by limits on theaccumulated size of a short-term map.

In some cases, resynchronization may be based on asynchronousreplication. For example, the snapshot-based resynchronization modeldiscussed above may also support another resynchronization model:storage systems that support asynchronous or periodic replication mayuse the snapshot mechanisms to replicate content duringresynchronization. An asynchronous or periodic replication model maypotentially copy out-of-date data during short periods of outage, whereperiodic replication models may be based on snapshots or checkpointdifferencing, and where the differencing automatically handles outages.As for asynchronous replication, there may be a reliance on snapshots orcheckpoints as a backup for extended outages, similarly to thediscussion above, and as a result, it may be practical to combineimplementations or to utilize such an available asynchronous or periodicreplication implementation for resynchronization. However, one issue maybe that asynchronous or periodic replication models may not beconfigured to get a replication target all the way up-to-date, orcompletely in-sync. As a result, with such resynchronizationimplementations, new in-flight operations may also be tracked so thatthe in-flight operations may be applied so that all modifications to anattaching storage system are current for a pod.

In some cases, resynchronization may be implemented to includemulti-phase resynchronization. For example, in a first phase, content upto some point may be replicated from in-sync storage systems for a podto an attaching storage system for a pod. In this example, a secondsnapshot may be taken during the attach and differences between a firstsnapshot that was the last snapshot known to have been synchronizedprior to a detach, and the differences between the first snapshot andthe second snapshot to be replicated to the attaching storage system.Such a mechanism may get the attaching storage system more closely insync than it was prior to the attach, however, it may still not beup-to-date. As such, a third snapshot may be created, and differencesbetween the third snapshot and the second snapshot may be determined,and then replicated to an attaching storage system. This third snapshot,and the determined differences, may make up part of the differencesbetween the content replicated up to the second snapshot and the currentcontent. Further, it is possible that additional snapshots may be takenand replicated to get within a few seconds of being up-to-date. At thispoint, modifying operations may be paused until a last snapshot isreplicated—thereby bringing the attaching storage system up-to-date forthe pod. In other cases, it is possible to switch after replicating oneor more snapshots to some mode where modifying operations that arereceived after a final resynchronization snapshot are handled in such away that they can be merged with the replicated snapshot content. Suchan implementation may include having the attaching storage system keeptrack of those modifying operations, and apply the modifying operationsafter the snapshot replication is complete—or after the snapshotreplication is known to have synchronized specific volume regionsaffected by particular modifying operation. This implementation may haveadditional overhead since tracking all operations until the underlyingcontent is known to have been copied may result in a large number oftracked operations. An alternative is to consider the content related torecently received operations, for example writes that rely on particularcommon metadata or extended copy operations from one block range toanother block range, and request that the resynchronization prioritizethe processing of that content or that common metadata information. Inthis way, any received operations tied to content known to have beencopied by such a process may then have tracking structures released muchmore quickly.

In some cases, resynchronization may be implemented to use directedacyclic graphs of logical extents. As described above, replicatedstorage systems may be based on directed acyclic graphs of logicalextents. In such a storage system, the process of resynchronization maybe expected to replicate the logical extent graphs from in-sync storagesystems for a pod to an attaching storage system for a pod—including allleaf logical extent content, and ensuring that the graphs aresynchronized and are being kept synchronized prior to enabling anattaching storage system as an in-sync pod member. Resynchronization inthis model may proceed by having a target storage system for an attachretrieve a top-level extent identity for each volume—or for each file orobject in a file or object based storage system. Any logical extentidentity that is already known to the attach target may be consideredup-to-date, but any unknown composite logical extent may be retrievedand then decomposed into underlying leaf or composite logical extents,each of which is either already known to the attach target or unknown tothe attach target. Further, any unknown leaf logical extent may retrievecontent, or can retrieve identities for stored blocks to determine ifthe block are already stored by the target storage system—withunrecognized blocks then being retrieved from an in-sync storage system.However, such an approach may not always result in resynchronizationbecause some number of extents from around a time of a detach of astorage system may have a same identity, but different content, becauseonly operations that marked logical extents are read-only may form newlogical extents as a result of modifying operations. Further,in-progress modifying operations may have completed differently ondifferent storage systems during faults that lead to a detach, and ifthose modifying operations were to non-read-only logical extents, thenthose logical extents may have the same identity on two storage systemsbut have different content. However, some solutions include when one setof storage systems for a pod detach another storage system, the set ofstorage systems may mark the leaf and composite logical extentsassociated with in-progress modifying operations, and associate thoseleaf and composite logical extents with a future reattach operation thatincludes the detached storage system. Similarly, the reattaching storagesystem for a pod may identify the leaf and composite logical extentsthat it knew of that were associated with in-progress logical extents.As a result, two sets of logical extents whose contents (for leafextents), or whose references (for composite logical extents), may needto be transferred in addition to transferring any unknown leaf orcomposite logical extents. Alternatively, coordinated snapshots may betaken periodically within replicated pods, and a target of a reattachoperation may ensure that logical extents created after the lastcoordinated snapshot are discarded or ignored during resynchronization.As yet another alternative, during a time period for which a storagesystem is detached from a pod, the remaining in-sync storage systems maymanufacture a snapshot that represents the content from all completedoperations, that that replays all potentially in-progress operations toapply to pod content that post-dates the snapshot—this results in anycontent not already replicated to the detached storage system beinggiven new logical extent identities that the detached storage system maynever have received.

Another issue that may face resynchronization implementations is gettingextent graph based synchronous replication fully synchronized andrunning live. For example, resynchronization may proceed by firsttransferring a more recent snapshot, such as one created at a beginningof an attach, by having the target storage system retrieve it fromin-sync storage systems in the manner described above, where the targetincrementally requests leaf and composite logical extents that it doesnot have. This process may include accounting for in-progress operationsat the time of the detach, where at the end of this process, the contentup to that more recent snapshot is synchronized between the in-syncstorage systems for the pod and the attaching storage system. Further,this process may be repeated with another snapshot, and possibly withadditional snapshots, to get the target storage system closer to thein-sync storage system. However, at some point, the live data may alsohave to be transferred, and to do this, replication of live modifyingoperations may be enabled for transfer to an attaching storage systemafter a last resynchronization snapshot, such that all modifyingoperations not included in the snapshot may be delivered to theattaching storage system. This implementation results in operations thatdescribe modifications to leaf and composite logical extents that areincluded in the snapshot, where these descriptions may include thecreate of new leaf and composite logical extents (with specifiedcontent) or the replacement of existing leaf and composite logicalextents with modified copies of those extents with new identities. Incases where an operation description creates new logical extents orreplaces logical extents already known to an attaching storage system,the operation may be processed normally as if the attaching storagesystem is in-sync. In cases where the description for an operationsincludes at least one replacement of a logical extent not already knownto an attaching storage system, that operation may be made durable toallow completion, but full processing of the operation may be delayeduntil the logical extent being replaced is received. Further, to reduceoverhead associated with these operations that are waiting for such alogical extent content transfer, an attaching storage system mayprioritize those logical extents to be retrieved earlier than otherlogical extents. In this example, in dependence upon how efficiently astorage system can handle these operations awaiting such a pre-existinglogical extent, there may be no reason to transfer any sequence ofsnapshot images prior to enabling live operations. Instead, aresynchronization snapshot that describes state information from a timeof a detach (or from some time prior to the detach) could betransferred—with operations being processed as described earlier, whiletransferring the snapshot from in-sync storage systems to the attachingstorage system, also as described earlier.

In some cases, an issue that may face a resynchronization implementationis preserving block references during a resynchronization. For example,in a synchronously replicated storage system, a specific written block,or a specific set of blocks associated with an operation, may be givenan identify that is included in the operation description for the writeof that block, or that block set. In this example, a new write thatreplaces that block, or some or all of the block set, may supply a newidentity for the block or block set, where this new identity may bebuilt from a secure hash of the block content (such as using SHA-256 orsome other mechanism that has a suitably infinitesimal chance ofdiffering blocks yielding the same hash value), or the new identity maysimply identity the write itself in a unique way irrespective of whethertwo writes included identical block contents. For example, the newidentity may be a sequence number or a timestamp. Further, if the newidentity for a block or block set is shared in the distributeddescription of a write operation and stored in some map in each storagesystem as part of writing the block or block set, then leaf logicalextents may describe their content in terms of these block or block setidentities. In such implementations, resynchronization of leaf extentsmay reference blocks or block sets already stored in an attachingstorage system rather than transferring them from an in-sync storagesystem. This implementation may reduce the total data transferred duringa resynchronization. For example, data which had already been written tothe attaching storage system around the time of a detach, but that wasnot included in a resynchronization snapshot, may have been stored withthat identity, and may not need to be transferred again because thatblock or block set identity is already known and stored. Further, ifsome number of virtual extended copy operations resulted in the copyingof block references between two leaf logical extents during the timethat a storage system was detached, then the block or block setidentities may be used to ensure that the virtually copied blocks arenot transferred twice.

In some cases, resynchronization implementations may usecontent-addressable stores, where stored blocks may have a uniqueidentity that may be based on a secure hash of block content. In thisexample, resynchronization may proceed by transferring a list of allblock identities related to a pod on in-sync storage systems to anattaching storage system, along with a mapping of those blocksidentities to volumes (or files or objects) in the pod. In this case, anattach operation may proceed by transferring these blocks the attachingstorage system is not aware of from an in-sync storage system for thepod—which may be integrated with processing of live operations thatchange a mapping from volume to content. Further, if some earlierversion of a mapping from pod content to block identities is known frombefore a storage system detach from the pod, then differences betweenthat earlier version and the current version may be transferred insteadof transferring an entire mapping.

As described above, metadata may be synchronized among storage systemsthat are synchronously replicating a dataset. Such metadata may bereferred to as common metadata, or shared metadata, that is stored by astorage system on behalf of a pod related to the mapping of segments ofcontent stored within the pod to virtual address within storage objectswithin the pod, where information related to those mappings issynchronized between member storage systems for the pod to ensurecorrect behavior—or better performance—for storage operations related tothe pod. In some examples, a storage object may implement a volume or asnapshot. The synchronized metadata may include: (a) information to keepvolume content mappings synchronized among the storage systems in thepod; (b) tracking data for recovery checkpoints or for in-progress writeoperations; (c) information related to the delivery of data and mappinginformation to a remote storage system for asynchronous or periodicreplication.

Information to keep volume content mappings synchronized among thestorage systems in the pod may enable efficient creating of snapshots,which in turn enables that subsequent updates, copies of snapshots, orsnapshot removals may be performed efficiently and consistently acrossthe pod member storage systems.

Tracking data for recovery checkpoints or for in-progress writeoperations may enable efficient crash recovery and efficient detectionof content or volume mappings that may have been partially or completelyapplied on individual storage systems for a pod, but that may not havebeen completely applied on other storage systems for the pod.

Information related to the delivery of data and mapping information to aremote storage system for asynchronous or periodic replication mayenable more than one member storage system for a pod to serve as asource for the replicated pod content with minimal concerns for dealingwith mismatches in mapping and differencing metadata used to driveasynchronous or periodic replication.

In some examples, shared metadata may include descriptions for, orindications of, a named grouping, or identifiers for, of one or morevolumes or one or more storage objects that are a subset of an entiresynchronously replicated dataset for a pod—where such a of volumes orstorage objects of a dataset may be referred to as a consistency group.A consistency group may be defined to specify a subset of volumes orstorage objects of the dataset to be used for consistent snapshots,asynchronous replication, or periodic replication. In some examples, aconsistency group may be calculated dynamically, such as by includingall volumes connected to a particular set of hosts or host networkports, or that are connected to a particular set of applications orvirtual machines or containers, where the applications, virtualmachines, or containers may operate on external server systems or mayoperate on one or more of the storage systems that are members of a pod.In other examples, a consistency group may be defined according to userselections of a type of data or set of data, or specifications of aconsistency group similar to the dynamic calculation, where a user mayspecify, for example through a command or management console, that aparticular, or named, consistency group be created to include allvolumes connected to a particular set of hosts or host network ports, orbe created to include data for a particular set of applications orvirtual machines or containers.

In an example using a consistency group, a first consistency groupsnapshot of a consistency group may include a first set of snapshot forall volumes or other storage objects that are members of the consistencygroup at the time of the first dataset snapshot, with a secondconsistency group snapshot of the same consistency group including asecond set of snapshots for the volumes or other storage objects thatare members of the consistency group at the time of the second datasetsnapshot. In other examples, a snapshot of the dataset may be stored onone or more target storage systems in an asynchronous manner. Similarly,asynchronous replication of a consistency group may account for dynamicchanges to member volumes and other storage objects of the consistencygroup, where consistency group snapshots of the consistency group ateither the source or the target of the asynchronous replication linkinclude the volumes and other storage objects that are members inrelationship to the consistency group at the time that the datasetsnapshot relates to. In the case of a target of an asynchronousreplication connection, the time that the dataset snapshot relates todepends on the dynamic dataset of the sender as it was received and wasin process at the time of the consistency group snapshot on the target.For example, if a target of an asynchronous replication is, say, 2000operations behind, where some of those operations are consistency groupmember changes, where a first set of such changes are more than 2000operations ago for the source, and a second set of changes are withinthe last 200, then a consistency group snapshot at that time on thetarget will account for the first set of member changes and will notaccount for the second set of changes. Other uses of the target ofasynchronous replication may similarly account for the nature of thetime of the dataset for the consistency group in determining the volumesor other storage objects (and their content) for those uses. Forexample, in the same case of asynchronous replication being 2000operations behind, use of the target for a disaster recovery failovermight start from a dataset that includes the volumes and other storageobjects (and their content) as they were 2000 operations ago at thesource. In this discussion, concurrent operations at the source (e.g.,writes, storage object creations or deletions, changes to propertiesthat affect inclusion or exclusion of volumes or other storage objectsor other data from a consistency group, or other operations that were inprogress and not signaled as completed at a same point in time) mightnot have a single well-defined ordering, so the count of operations onlyneeds to represent some plausible ordering based on any allowed orderingof concurrent operations on the source.

As another example using consistency groups, in the case of periodicreplication based on replication of consistency group snapshots, eachreplicated consistency group snapshot would include the volumes andother storage objects at the time each consistency group snapshot wasformed on the source. Ensuring that membership in a consistency group iskept consistent by using common, or shared, metadata, ensures that afault—or other change which may cause the source of replication, or thesystem that forms a dataset snapshot, to switch from one storage systemin a pod to another—does not lose information needed for properlyhandling those consistency group snapshots or the consistency groupreplication. Further, this type of handling may allow for multiplestorage systems that are members of a pod to concurrently serve assource systems for asynchronous or periodic replication.

Further, synchronized metadata describing mapping of segments to storageobjects is not limited to mappings themselves, and may includeadditional information such as sequence numbers (or some other value foridentifying stored data), timestamps, volume/snapshot relationships,checkpoint identities, trees or graphs defining hierarchies, or directedgraphs of mapping relationships, among other storage system information.

Readers will appreciate that the methods described above may be carriedout by any combination of storage systems described above. Furthermore,any of the storage systems described above may also pair with storagethat is offered by a cloud services provider such as, for example,Amazon™ Web Services (‘AWS’), Google™ Cloud Platform, Microsoft™ Azure,or others. In such an example, members of a particular pod may thereforeinclude one of the storage systems described above as well as a logicalrepresentation of a storage system that consists of storage that isoffered by a cloud services provider. Likewise, the members of aparticular pod may consist exclusively of logical representations ofstorage systems that consist of storage that is offered by a cloudservices provider. For example, a first member of a pod may be a logicalrepresentation of a storage system that consists of storage in a firstAWS availability zone while a second member of the pod may be a logicalrepresentation of a storage system that consists of storage in a secondAWS availability zone.

To facilitate the ability to synchronously replicate a dataset (or othermanaged objects such as virtual machines) to storage systems thatconsist of storage that is offered by a cloud services provider, andperform all other functions described in the present application,software modules that carry out various storage system functions may beexecuted on processing resources that are provided by a cloud servicesprovider. Such software modules may execute, for example, on one or morevirtual machines that are supported by the cloud services provider suchas a block device Amazon™ Machine Image (‘AMI’) instance. Alternatively,such software modules may alternatively execute in a bare metalenvironment that is provided by a cloud services provider such as anAmazon™ EC2 bare metal instance that has direct access to hardware. Insuch an embodiment, the Amazon™ EC2 bare metal instance may be pairedwith dense flash drives to effectively form a storage system. In eitherimplementation, the software modules would ideally be collocated oncloud resources with other traditional datacenter services such as, forexample, virtualization software and services offered by VMware™ such asvSAN™. Readers will appreciate that many other implementations arepossible and are within the scope of the present disclosure.

Readers will appreciate that in situations where a dataset or othermanaged object in a pod is retained in an on-promises storage system andthe pod is stretched to include a storage system whose resources areoffered by a cloud services provider, the dataset or other managedobject may be transferred to the storage system whose resources areoffered by a cloud services provider as encrypted data. Such data may beencrypted by the on-promises storage system, such that the data that isstored on resources offered by a cloud services provider is encrypted,but without the cloud services provider having the encryption key. Insuch a way, data stored in the cloud may be more secure as the cloud hasno access to the encryption key. Similarly, network encryption could beused when data is originally written to the on-premises storage system,and encrypted data could be transferred to the cloud such that the cloudcontinues to have no access to the encryption key.

Through the use of storage systems that consist of storage that isoffered by a cloud services provider, disaster recovery may be offeredas a service. In such an example, datasets, workloads, other managedobjects, and so on may reside on an on-premises storage system and maybe synchronously replicated to a storage system whose resources areoffered by a cloud services provider. If a disaster does occur to theon-premises storage system, the storage system whose resources areoffered by a cloud services provider may take over processing ofrequests directed to the dataset, assist in migrating the dataset toanother storage system, and so on. Likewise, the storage system whoseresources are offered by a cloud services provider may serve as anon-demand, secondary storage system that may be used during periods ofheavy utilization or as otherwise needed. Readers will appreciate thatuser interfaces or similar mechanisms may be designed that initiate manyof the functions described herein, such that enabling disaster recoveryas a service may be as simple as performing a single mouse click.

Through the use of storage systems that consist of storage that isoffered by a cloud services provider, high availability may also beoffered as a service. In such an example, datasets, workloads, othermanaged objects, that may reside on an on-premises storage system may besynchronously replicated to a storage system whose resources are offeredby a cloud services provider. In such an example, because of dedicatednetwork connectivity to a cloud such as AWS Direct Connect,sub-millisecond latency to AWS from variety of locations can beachieved. Applications can therefore run in a stretched cluster modewithout massive expenditures upfront and high availability may beachieved without the need for multiple, distinctly located on-premisesstorage systems to be purchased, maintained, and so on. Readers willappreciate that user interfaces or similar mechanisms may be designedthat initiate many of the functions described herein, such that enablingapplications may be scaled into the cloud by performing a single mouseclick.

Through the use of storage systems that consist of storage that isoffered by a cloud services provider, system restores may also beoffered as a service. In such an example, point-in-time copies ofdatasets, managed objects, and other entities that may reside on anon-premises storage system may be synchronously replicated to a storagesystem whose resources are offered by a cloud services provider. In suchan example, if the need arises to restore a storage system back to aparticular point-in-time, the point-in-time copies of datasets and othermanaged objects that are contained on the storage system whose resourcesare offered by a cloud services provider may be used to restore astorage system.

Through the use of storage systems that consist of resources that areoffered by a cloud services provider, data that is stored on anon-premises storage system may be natively piped into the cloud for useby various cloud services. In such an example, the data that is in itsnative format as it was stored in the on-premises storage system, may becloned and converted into a format that is usable for various cloudservices. For example, data that is in its native format as it wasstored in the on-premises storage system may be cloned and convertedinto a format that is used by Amazon™ Redshift such that data analysisqueries may be performed against the data. Likewise, data that is in itsnative format as it was stored in the on-premises storage system may becloned and converted into a format that is used by Amazon™ DynamoDB,Amazon™ Aurora, or some other cloud database service. Because suchconversions occurs outside of the on-premises storage system, resourceswithin the on-premises storage system may be preserved and retained foruse in servicing I/O operations while cloud resources that can bespun-up as needed will be used to perform the data conversion, which maybe particularly valuable in embodiments where the on-premises storagesystem operates as the primary servicer of I/O operations and thestorage systems that consist of resources that are offered by a cloudservices provider operates as more of a backup storage system. In fact,because managed objects may be synchronized across storage systems, inembodiments where an on-premises storage system was initiallyresponsible for carrying out the steps required in an extract,transform, load (‘ETL’) pipeline, the components of such a pipeline maybe exported to a cloud and run in a cloud environment. Through the useof such techniques, analytics as a service may also be offered,including using point-in-time copies of the dataset (i.e., snapshots) asinputs to analytics services.

Readers will appreciate that applications can run on any of the storagesystems described above, and in some embodiments, such applications canrun on a primary controller, a secondary controller, or even on bothcontrollers at the same time. Examples of such applications can includeapplications doing background batched database scans, applications thatare doing statistical analysis of run-time data, and so on.

For further explanation, FIG. 10 sets forth a flow chart illustrating anexample method for application replication among storage systemssynchronously replicating a dataset according to some embodiments of thepresent disclosure. Although depicted in less detail, the first storagesystem (1002) and the second storage system (1010) depicted in FIG. 10may be similar to the storage systems described above with reference toFIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3C, FIGS. 4-9, or any combinationthereof. In fact, the first storage system (1002) and the second storagesystem (1010) depicted in FIG. 10 may include the same, fewer,additional components as the storage systems described above. In someembodiments, the first storage system (1002) or the second storagesystem (1010) may be a cloud-based storage system. In some embodiments,both the first storage system (1002) and the second storage system arecloud-based storage systems.

In the example depicted in FIG. 10, the storage system (1002) includesan application (1004) deployed on a platform (1006) coupled with thestorage system (1002). In some examples, the application (1004) may beembodied as one or more modules of computer program instructionsexecuting on computer hardware such as a computer processor of thestorage system (1002), and may be separate from an operating environmentfor servicing I/O requests from users of the storage system, writingdata to the storage devices in the storage system, reading data from thestorage devices in the storage system, performing garbage collection onthe storage devices in the storage system, and performing otheroperations described above. In these examples, the storage system (1002)may be a physical storage system that hosts the application (1004). Inother examples, the application (1004) may be embodied as one or moremodules of computer program instructions executing within a virtualcomponent that is coupled to the storage system (1002). In theseexamples, the storage system (1002) may be a virtual storage systemcoupled to a virtual component (e.g., a virtual compute node) that hoststhe application (1004).

The application (1004) may use the storage system (1002) in implementingfile systems, data objects, or databases, or may implement backgroundoperations such as background batched database scans or statisticalanalysis of run-time data. In this context, the application runningwithin or coupled to the storage system may be part of a largerapplication. For example, the coupled application may provide keydurability services for, say, an internet banking service, while theoverall application providing the banking service itself may beimplemented using any number of additional servers or data center orcloud components. Applications such as application (1004) may providefunctionality that is dependent upon the storage system (1002) beingin-sync and online with other storage systems (e.g., storage system(1010)), where any of these applications may be distributedimplementations that operate on a synchronously replicated, andsymmetrically accessible, underlying storage implementation. In oneexample, applications or services such as application (1004) may behosted on respective platforms running in two locations that userespective storage systems corresponding to those two locations, wherethese applications or services (or components of the applications orservices that use a particular set of storage) run in one location at atime with a storage system being available for access in the locationthat is currently running those components. In this example, the storeddata is synchronously replicated to the other location so that, in theevent of a fault, the application or service can be brought up in thealternate location with no data loss.

The application (1004) utilizes the computing resources of the storagesystem (1002) or a virtual component coupled to the storage system(1002), rather than executing remotely on a host, thus reducing latencybetween the application (1004) and the data stored on the storage system(1002). The computing resources utilized by the application (1004) maybe provided by the platform (1006) in that the application (1004) ishosted on the platform (1006) as, for example, an application resident,deployed, or instantiated on the platform (1006).

In some examples, the storage system (1002) may also include a secondplatform (1008). The first platform (1006) and the second platform(1008) may be embodied in a variety of ways, so long as the firstplatform (1006) and the second platform (1008) are able to support theexecution of the application (1004). The first platform (1006) and thesecond platform (1008) may be embodied, for example, as virtual machinesthat are executing on computer hardware within or coupled to the storagesystem (1002), as storage controllers that are included within orcoupled to the storage system (1002), as servers that are includedwithin or coupled to the storage system (1002), or as any combination ofsuch examples. In some examples, the first platform (1006) and thesecond platform (1008) are embodied in redundant storage controllers ina dual controller storage system, in that the storage system (1002) caninclude at least one active controller and at least one redundantcontroller, which may be similar to the storage array controllersdescribed above. In such an example, a redundant controller can serve asa passive controller in an active-passive pair of controllers, where anactive controller serves as the controller that issues I/O requests fromexternal hosts to storage devices within the storage system (1002).Readers will appreciate that while the active controller and theredundant controller may be part of an active-passive pair, such anembodiment is only one possible embodiment of the present disclosure.The first controller and the redundant controller may be part of anactive-active pair or configured in some other manner, so long as thereare redundant resources.

In one example, the platform (1006) hosting the application (1004) is asecondary controller of a primary/secondary dual controller storagesystem. As explained above, for high availability of data in the storagesystem, the passive secondary controller may be promoted to an activesecondary controller in the event of a fault in the previously-activeprimary controller. Readers will appreciate that, while the secondarycontroller is serving as a passive redundant controller, the computingresources of the secondary controller may be underutilized. As such, thesecondary controller may be ideal for hosting applications such as theapplication (1004) that may utilize the computing resources of thesecondary controller to implement file systems or databases, or provideanalytics and batch processing functions, particularly where operationsare optimized by close proximity to the data stored in the storagesystem. In the event that the primary controller fails over to thesecondary controller, the application may be terminated to free upcomputing resources needed to service I/O requests from remote hosts.However, in some examples, the application (1004) may be deployed on theactive primary controller.

In some implementations, the primary controller is primarily dedicatedto servicing I/O requests received from externals hosts, while thesecondary controller is not visible to those hosts. In one example, theprimary controller services I/O requests directed to a dataset on thestorage system that are received from remote hosts, while the secondarycontroller may service I/O requests directed to a dataset on the storagesystem that are received from resident applications. In some examples,the first platform (1006) and the second platform (1008) may beredundant virtual controllers of a cloud-based virtual storage system.

While the example of FIG. 10 depicts the first storage system (1002) ashaving two platforms (e.g., two storage controllers), readers willappreciate that a storage system may include more or fewer storagecontrollers. For example, a storage system may include a single storagecontroller. In such an example, the single storage controller in astorage system may serve as a passive secondary passive controller,where a controller in another storage system serves as the activeprimary controller. Other storage controllers in a storage system may beneither primary or secondary storage controllers, and may instead beused to facilitate communication between the primary and secondarystorage controllers.

The example method of FIG. 10 includes storing (1020), on a firststorage system (1002), a dataset (1058) that is synchronously replicatedacross a plurality of storage systems. In the example depicted in FIG.10, the dataset (1058) is synchronously replicated across the firststorage system (1002) and a second storage system (1010), althoughreaders will appreciate that the dataset (1058) may be synchronouslyreplicated across other storage systems in addition to those depicted.The second storage system (1010) depicted in FIG. 10 may be similar tothe storage system (1002) and to the storage systems described abovewith reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3C, FIGS. 4-9, orany combination thereof. In fact, the storage system (1010) depicted inFIG. 10 may include the same, fewer, or additional components as thestorage systems described above. In some embodiments, the second storagesystem (1010) may be a cloud-based storage system. In some examples, thefirst storage system (1002) and the second storage system (1010) thatare synchronously replicating the dataset (1058) may be members of a pod(1070) having the attributes of a pod as described above. The dataset(1058) may be stored in storage resources (1056) of the first storagesystem, such as any of the storage devices and storage arrays describedabove. The dataset (1058) may also be stored in storage resources (1060)of the second storage system, such as any of the storage devices andstorage arrays described above.

In the example depicted in FIG. 10, storing (1020), on a first storagesystem (1002), a dataset (1058) that is synchronously replicated acrossa plurality of storage systems is carried out by storing the dataset(1058) in storage resources of the first storage system (1002) as wellas synchronously replicating, to the second storage system (1010), I/Ooperations directed to the dataset (1058) that are received by the firststorage system (1002), and applying changes to the dataset (1058) thatare received from the second storage system (1010), utilizing thesynchronous replication techniques described above. In some examples,synchronous replication entails acknowledging to a consumingapplication, by a storage system that receives a request directed thedataset, that the request has been completed after receivingconfirmation that the modification has been applied from other storagesystems synchronously replicating the dataset. In some embodiments,asynchronous replication techniques such as those discussed above may beused to replicate the dataset (1058) across a plurality of storagesystems (e.g., the first storage system (1002) and the second storagesystem (1010)). In such examples, asynchronous replication entailsacknowledging to a consuming application, by a storage system thatreceives a request directed the dataset, that the request has beencompleted without waiting for other storage systems replicating thedataset to confirm that the modification has been applied. For example,checkpoints may be used to synchronize transactions on the storagesystems at defined intervals, or snapshot-based replication may be usedto asynchronously replicate one storage system (e.g., storage system1002) onto another storage system (e.g., storage system 1010) during afailover or disaster recovery scenario.

The example method of FIG. 10 also includes identifying (1022), by thefirst storage system (1002), one or more input/output (I/O) requests(1012) directed to the dataset (1058), wherein the one or more I/Orequests (1012) are initiated by an application (1004) coupled with aplatform (1006) of the first storage system (1002). Identifying (1022),by the first storage system (1002), one or more input/output (I/O)requests (1012) directed to the dataset (1058), wherein the one or moreI/O requests (1012) are initiated by an application (1004) hosted on aplatform (1006) of the first storage system (1002) may be implementedusing a communication channel that is internal to the storage system(1002) to receive the I/O requests from the application (1004).

In some examples, the application (1004) implements a file system, dataobjects, or database that requires the dataset (1058) stored on thefirst storage system (1002) that requires the dataset (1058) to bein-sync with the instance of the dataset (1058) stored on the secondstorage system. In these examples, the application (1004) may be aclustered application, where distributed processing performed by theapplication may employ communication between instances of theapplication, and where modifications to the dataset (1058) made by theapplication (1004) on the first storage system (1002) are synchronouslyreplicated to the instance of the dataset (1058) stored on the secondstorage system (1010). In some examples, the application (1004) may bean analytics application or batch processing application that does notreceive I/O requests from external hosts.

The application (1004) may be hosted on the first platform (1006) of thestorage system (1002) in that the application (1004) is resident,deployed, or instantiated on the first platform (1006). In someexamples, the first platform (1006) is a storage controller, such thatthe application (1004) runs on the storage controller or within acontainer on the storage controller. In these examples, the firstplatform (1006) may be a primary storage controller or a secondarystorage controller of the first storage system (1002). For example, thefirst platform (1006) may be a passive secondary controller that doesnot receive I/O requests from external hosts except in the event thatthe secondary controller is promoted to an active controller (e.g., in afailover). In this example, the application (1004) utilizes theresources of the secondary controller that are typically underutilized.In some examples, the first platform (1006) includes a virtual machinehosted on or coupled to the storage system, in which the application isinstantiated or deployed. In these examples, the virtual machine mayhave dedicated computing resources (i.e., processor(s) and RAM) of thestorage system. In some examples, the application (1004) may be coupledwith the first platform (1006) of the storage system (1002) insteadbeing hosted on the platform, such as when the platform (1006) is avirtual component of a virtual storage system or cloud-based storagesystem.

The example method of FIG. 10 also includes servicing (1024), by thefirst storage (1002) system, the one or more I/O requests (1012)directed to the dataset (1058). The I/O requests (1012) may include, forexample, requests to read, write, copy, or invalidate data stored in thedataset (1058). Servicing (1024), by the first storage (1002) system,the one or more I/O requests (1012) directed to the dataset (1058) byperforming the I/O operation requested by the application (1004) on thefirst storage system (1002) and replicating the I/O operation, when theI/O operation modifies the dataset (1058), to the second storage system(1010).

Readers will appreciate that hosting the application on the storagecontroller of the storage system reduces the communication pathway, thusreducing I/O operation latency and increasing performance, by placingthe application closer to the stored data that if the application wereexecuted on an external host, which is advantageous for data intensiveapplications. Readers will also appreciate that hosting the applicationon a passive storage controller in a dual controller or scale outstorage system conserves computing resources of the active controller ofthe storage system. Readers will also appreciate that synchronousreplication of the dataset across a plurality of storage systems allowsfor application clustering and symmetric data access, such thatapplications hosted on disparate storage systems may utilize local,synchronized instances of the same dataset.

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method for application replication among storage systemssynchronously replicating a dataset according to some embodiments of thepresent disclosure. The example method of FIG. 11 is similar to theexample method of FIG. 10 in that the example method of FIG. 11 alsoincludes storing (1020), on a first storage system (1002), a dataset(1058) that is synchronously replicated across a plurality of storagesystems; identifying (1022), by the first storage system (1002), one ormore input/output (I/O) requests (1012) directed to the dataset (1058),wherein the one or more I/O requests (1012) are initiated by anapplication (1004) coupled with a platform (1006) of the first storagesystem (1002); and servicing (1024), by the first storage (1002) system,the one or more I/O requests (1012) directed to the dataset (1058).

The example method of FIG. 11 further includes deploying (1102) on thefirst platform (1006), a container (1106) for executing the application(1004). The application (1004) may be deployed in a variety of ways,including container-based deployment models. Containerized applicationsmay be deployed and managed using a variety of tools, including anoperating system level virtualization service (1104) of the firststorage system (1102), such as services that provide operating systemlevel virtualization, or containers. Examples of operating system levelvirtualization services include containerization services such asDocker™, hybrid cloud container orchestration such as Mesosphere™, andContainer Orchestration Service such as Kubernetes™ The operating systemlevel virtualization service (1104) may be embodied, for example, as amodule of computer software that, when executed on computer hardware,provides a managed environment for deploying containerized applications.The operating system level virtualization service (1104) may support theexecution of one or more containerized applications through the use of acontainer image. A container image may be embodied, for example, as alightweight, stand-alone, executable package that includes everythingneeded to run a piece of software (e.g., the containerized application).A container image can include, for example, a runtime or scriptscontaining the various parts of the application implementation,libraries, environment variables, configuration files, and so on. Thus,the container (1106) may be viewed as a run-time instance of a containerimage deployed through the operating system level virtualization service(1104).

In the example of FIG. 11, deploying (1102), on the first storage system(1002), a container (1106) executing the application (1004) may beimplemented by instantiating a container image including the application(1004). In some examples, instantiating a container image including theapplication (1004) is carried out by an operating system levelvirtualization service (1104) of the first storage system (1002). Thecontainer image for the containerized application may be provided by auser or may be resident on the storage system (1002). In some examples,the container image for the containerized application may be stored onmultiple storage systems such that, for example, any of the storagesystems synchronously replicating the dataset (1058) may spin up aninstance of the application (1004). In some cases, the application(1004) may be an analytics application that performs run-timeperformance analysis of the first storage system (1002) or any of thestorage systems that includes the operating system level virtualizationservice (1104) and the container image. In some examples, the secondstorage system (1010) also includes the operating system levelvirtualization service (not depicted) for hosting the containerizedapplication (1004). In addition to portability, the use of containers toexecute the application (1004) provides data isolation. For example, thecontainer (1106) may be configured to mount a specific volume, thuspreventing the application (1004) from modifying or accessing dataoutside the mounted volume.

In some embodiments, the container image for the application (1004) maybe synchronously replicated across the set of storage systemssynchronously replicating the dataset, such that the containerizedapplication may be spun up on any of the storage systems. Thus, anychanges made to the container image will be propagated to all of thestorage systems. Readers will appreciate that embodiments of the presentdisclosure alleviate the need to transfer the container image over adata communications link to the particular storage system that includesthe data for the containerized application because the container may bespun up on any storage system that is synchronously replicating thedataset. In such a way, through the use of synchronously replicateddatasets available to the operating system level virtualization serviceon any storage system synchronously replicating the dataset,containerized applications may be deployed in a way that does notconsume networking bandwidth.

For further explanation, FIG. 12 sets forth a flow chart illustrating anexample method for application replication among storage systemssynchronously replicating a dataset according to some embodiments of thepresent disclosure. The example method of FIG. 12 is similar to theexample method of FIG. 10 in that the example method of FIG. 12 alsoincludes storing (1020), on a first storage system (1002), a dataset(1058) that is synchronously replicated across a plurality of storagesystems; identifying (1022), by the first storage system (1002), one ormore input/output (I/O) requests (1012) directed to the dataset (1058),wherein the one or more I/O requests (1012) are initiated by anapplication (1004) coupled with a platform (1006) of the first storagesystem (1002); and servicing (1024), by the first storage (1002) system,the one or more I/O requests (1012) directed to the dataset (1058).

The example method of FIG. 12 further includes identifying (1202), by asecond storage system (1010) among the plurality of storage systemssynchronously replicating the dataset (1058), one or more I/O requests(1212) directed to the dataset (1058), wherein the one or more I/Orequests (1212) are initiated by a second instance of the application(1004) coupled with a platform (1206) of the second storage system(1010). Like the platform (1006) of the first storage system (1002), theplatform (1206) of the second storage system (1010) may host a secondinstance the application (1004), which may be resident, deployed orinstantiated on the platform (1206) of the second storage system (1010).In some examples, the application (1004) may be coupled with theplatform (1206) of the storage system (1010) instead being hosted on theplatform, such as when the platform (1206) is a virtual component of avirtual storage system or cloud-based storage system.

In some examples, as shown in the example depicted in FIG. 12, thesecond storage system (1010) may include redundant platforms (1206,1208). The platforms (1206, 1208) may be embodied, for example, asvirtual machines that are executing on computer hardware within thestorage system (1010), as storage controllers that are included withinthe storage system (1010), as servers that are included within thestorage system (1010), or as any combination of such examples. In someembodiments, the redundant platforms (1206, 1208) of the second storagesystem are redundant storage controllers as discussed above. Forexample, the platform (1206) may be a passive secondary storagecontroller and the platform (1208) may be an active primary controller.Where the computing resources of the secondary storage controller may beunderutilized, hosting the application (1004) on the passive secondarystorage controller increases the efficiency of resource consumption. Insome examples, neither of the platforms (1206, 1208) are designated asprimary storage controllers (i.e., active storage controllers). In theseexamples, the primary storage controller of the first storage system(1002) may also be the primary storage controller of the second storagesystem (1010).

In the example of FIG. 12, identifying (1202), by a second storagesystem (1010) among the plurality of storage systems synchronouslyreplicating the dataset (1058), one or more I/O requests (1212) directedto the dataset (1058), wherein the one or more I/O requests (1212) areinitiated by a second instance of the application (1004) coupled with aplatform (1206) of the second storage system (1010) may be carried outsimilarly to identifying (1022) one or more I/O requests by the firststorage system (1002) as discussed above. For example, identifying(1202), by a second storage system (1010) among the plurality of storagesystems synchronously replicating the dataset (1058), one or more I/Orequests (1212) directed to the dataset (1058) may be implemented usinga communication channel that is internal to the storage system (1010) toreceive the I/O requests from the second instance of the application(1004).

The method of FIG. 12 also includes servicing (1204), by the secondstorage system (1010), the one or more I/O requests (1212) directed tothe dataset (1058). Servicing (1204), by the second storage system(1010), the one or more I/O requests (1212) directed to the dataset(1058) may be carried out as discussed above, for example, by performingthe I/O operation requested by the second instance of the application(1004) on the second storage system (1010) and replicating the I/Ooperation, when the I/O operation modifies the dataset (1058), to thefirst storage system (1002).

In some embodiments, the application (1004) hosted on the first storagesystem (1002) and the second instance of the application (1004) hostedon the second storage system (1010) may be communicatively linked tooperate as a distributed application. For example, the application(1004) may be a database application or an implementation of a filesystem, in which the application instances act in concert on the dataset(1058) and share information about operations on the dataset (1058)(e.g., workload completion, file system changes). In some examples,communication link between the application (1004) hosted on the firststorage system (1002) and the second instance of the application (1004)hosted on the second storage system (1010) may be implemented by usingone or more communication protocols for transporting packets or dataacross a network, such as a storage area network (158), the Internet, orany computer network across the applications on the storage systems maycommunicate. The application instances may communicate by sendingmessages directly between the applications or through the respectivestorage controllers hosting the applications.

For further explanation, FIG. 13 sets forth a flow chart illustrating anexample method for application replication among storage systemssynchronously replicating a dataset according to some embodiments of thepresent disclosure. The example method of FIG. 13 is similar to theexample method of FIG. 10 in that the example method of FIG. 13 alsoincludes storing (1020), on a first storage system (1002), a dataset(1058) that is synchronously replicated across a plurality of storagesystems; identifying (1022), by the first storage system (1002), one ormore input/output (I/O) requests (1012) directed to the dataset (1058),wherein the one or more I/O requests (1012) are initiated by anapplication (1004) coupled with a platform (1006) of the first storagesystem (1002); and servicing (1024), by the first storage (1002) system,the one or more I/O requests (1012) directed to the dataset (1058).

The example method of FIG. 13 further includes migrating (1302) theapplication from the first storage system (1002) to a platform (1306) ofa second storage system (1010) among the plurality of storage systemssynchronously replicating the dataset (1058). The platform (1306) may beembodied, for example, as a virtual machine that is executing oncomputer hardware within the second storage system (1010), as a storagecontroller that is included within the second storage system (1010), asa server that is included within the second storage system (1010), or asany combination of such examples. In particular embodiments, theplatform (1306) is embodied in a storage controller of the secondstorage system (1010). In some implementations, the second storagesystem (1010) may include a second platform (not depicted) that may beembodied, for example, in a redundant storage controller in the storagesystem (1010). In some examples, one of the redundant storagecontrollers is designated as a primary storage controller, while theother is designated as a secondary storage controller.

In the example of FIG. 13, migrating (1302) the application from thefirst storage system (1002) to a platform (1306) of a second storagesystem (1010) among the plurality of storage systems synchronouslyreplicating the dataset (1058) may be carried out by detecting that theapplication (1004) should be migrated, rejecting pending I/O operationsfrom the application (1004) on the first storage system, and initiatingexecution of the application (1004) on the second storage system (1010).For example, detecting that the application (1004) should be migratedcan include detecting a failover from a storage controller of the firststorage system (1002) to a storage controller of the second storagesystem (1010), or determining that the application (1004) should bemigrated as part of a load balancing operation. Initiating execution ofthe application (1004) on the second storage system (1010) may include,for example, provisioning the application (1004) on the second storagesystem (1010), instantiating a resident copy of the application (1004)on the second storage system, or deploying the application (1004) as acontainerized application.

In some embodiments, asynchronous replication techniques may be used toreplicate the dataset (458) among a plurality of storage systems such asthe first storage system (1002) and the second storage system (1010). Insuch cases, a promoted/demoted model may be employed for handling I/Orequests and synchronizing the dataset (458) in which one storage systemis designated as the promoted storage system for the dataset (458) andall other storage systems replicating the dataset (458) are demotedstorage systems. The promoted storage system services I/O requests fromthe application (1004) directed to the dataset (458), whereas thedemoted system does not service I/O requests from the application(1004). Read requests are serviced by the promoted storage system andwrite requests (or other modifying operations) are acknowledged by thepromoted storage system without waiting for synchronization with thedemoted storage systems. In one example, to synchronize the dataset(458) on the demoted storage systems, the promoted storage systemmaintains a journal of modifications to the dataset (458) resulting fromthe completed write requests. In some cases, the journal functions as aqueue of write operations that are to be replicated on the demotedstorage systems. The demoted storage systems use the journal to updatetheir local copy of the dataset (458). As such, a replication target(i.e., a demoted storage system) tends to lag slightly behind thereplication source (i.e., a promoted storage system), resulting innearly synchronous replication to the demoted storage systems. Inanother example, to synchronize the dataset (458) on the demoted storagesystems, the promoted storage system copies a snapshot of the dataset(458) to the demoted storage systems, for example, at a periodicinterval or after a particular number of write operations have beenlogged.

Consider an example where the first storage system (1002) is a promotedstorage system and the second storage system (1010) is a demoted storagesystem with respect to the dataset (458). In the event of a failure orother performance degradation in the first storage system (1002), thesecond storage system (1010) may be promoted. In some scenarios, thesecond storage system (1010) may be promoted as part of readinesstesting of a failover to the second storage system (1010) (also referredto as “fire drills”) from another storage system (e.g., the firststorage system (1002)). As part of promoting the second storage system(1010) any write requests that were in-flight to the first storagesystem (1002) must be rejected or redirected to the second storagesystem (1010), and any journaled operations that have not been processedby the second storage system (1010) must be processed, to ensure anup-to-date copy of the dataset (458). Moreover, the application (1004)executing on the first storage system should be migrated to andconfigured on the second storage system (1010). In this example, theapplication (1004) may be migrated from, e.g., the platform (1006) ofthe first storage system (1002), to the platform (1306) of the secondstorage system (1010) in response to the second storage system (1010)being promoted. Migrating the application (1004) in response to thesecond storage system (1010) being promoted may include, for example,provisioning the application (1004) on the second storage system (1010),instantiating a resident copy of the application (1004) on the secondstorage system, or deploying the application (1004) as a containerizedapplication on the second storage system (1010). In some examples,migrating the application (1004) in response to the second storagesystem (1010) being promoted may include decoupling the application(1004) from the first storage system (1002) and coupling the application(1004) with the second storage system (1010), where the first storagesystem (1002) and the second storage system (1010) are virtual orcloud-based storage systems. Additional details regarding promotion anddemotion of storage systems, including the integration of coupledapplication management with fire drills can be found in U.S. patent Ser.Nos. 16/668,794, 16/669,038, and 16/668,664, each of which isincorporated herein for all purposes.

A data path between a host and a storage system can be establishedthrough a variety of connectivity mediums such as iSCSI or FiberChannel. In one example, where iSCSI is used, a host (i.e., a hostinitiator) is configured with an address (e.g., an IP address) of aniSCSI discovery service. As part of a discovery protocol, the hostqueries the iSCSI discovery service for a list of iSCSI targets. Thediscovery service provides a list of name space identifiers (e.g., iSCSIqualified names (IQNs) and target IP addresses for all of the targetIQNs to which the host is allowed to connect. The host then connects tothe iSCSI target storage system (potentially through multiple paths)using the target IP addresses. Once logged in, the host can access anylogical unit numbers (LUNs) advertised through the target IQN. One issuewith this approach is the potential mismatch between a target IQN andLUNs. That is, it is difficult to move a volume from appearing behindone IQN target to being exposed from another IQN target withoutreconfiguring the host.

In accordance with some embodiments, this challenge is addressed byvirtualizing a data path by mapping a target namespace identifier (e.g.,a target IQN) per volume or for an identifiable group of volumes. Thisallows, for example, for the migration of a volume (or an identifiablegroup of volumes) from one storage system to another while maintaining aconsistent mapping of logical host connections to the underlying storagesystem serving the volume or volumes. This also facilitates, forexample, for migrating host access to a dataset between storage systemssynchronously replicating the dataset.

For further explanation, FIG. 14 sets forth a flow chart illustrating anexample method of data path virtualization according to some embodimentsof the present disclosure. The example of FIG. 14 includes mapping 1404a virtual namespace identifier 1402 to one or more volumes 1430, 1432stored among a pool 1410 of storage resources, wherein a plurality ofstorage systems 1412, 1414 are utilized to provide the storageresources. Although depicted in less detail, storage systems 1412, 1414depicted in FIG. 14 may be similar to the storage systems describedabove with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3D, FIGS.4-13, or any combination thereof. In fact, the storage systems 1412,1414 depicted in FIG. 14 may include the same, fewer, or additionalcomponents as the storage systems described above. Although only twostorage systems 1412, 1414 are depicted, it will be appreciated that anynumber of storage systems may be utilized to provide the pool 1410 ofstorage resources.

In the example depicted in FIG. 14, the storage systems 1412, 1414provide storage resources to one or more hosts 1422. To that end, eachstorage system 1412, 1414 includes one or more storage controllers forproviding I/O access and data services for volumes serviced by thatstorage system. A volume can be, for example, a raw block volume, filesystem volume, or an object store bucket. A host 1422 can connect to thestorage controllers of each storage system 1412, 1414 to establish adata path for accessing the volume. In some examples, multiple datapaths between a host and a particular storage controller are establishedfor failover or load balancing purposes. A data path can be establishedthrough a variety of connection mediums, such as iSCSI,NVMe-over-Fabrics (NVMe-oF), SCSI over Fibre Channel, and other suitableprotocols as recognized by those of skill in the art. A storage systemendpoint for access to the volume can be a network address of a storagecontroller, such as an IP address, MAC address, port, virtual networkaddress, or Fibre Channel ID, or combinations thereof.

In the example depicted in FIG. 14, the storage systems 1412, 1414 arefederated storage systems that provide pool 1410 of storage resourcesmade available to one or more hosts 1422 through a storage orchestrationservice 1408. In some examples, the storage orchestration servicepresents to the host 1422 as a single abstracted storage system thatprovides storage resources. The storage orchestration service 1408utilizes storage systems 1412, 1414 to provide those storage resources.Moreover, the storage orchestration service 1408 effectively virtualizesa data path as a logically managed connection between the host and acollection of storage system volumes such that the host remains agnosticto which physical storage system services each volume. The storageorchestration service 1408 coordinates a data path between a host 1422and an endpoint of the storage system that services a particular volume.As such, the storage orchestration service 1408 presents pool 1410 ofstorage resources to the host 1422, where the storage orchestrationservice 1408 utilizes the storage systems 1412, 1414 to provide the pool1410 of storage resources.

As depicted in FIG. 14, the storage system 1412 stores two volumes 1430,1432. For example, those volumes 1430, 1432 may have been originallyprovisioned by the storage orchestration service on the storage system1412. However, it will be appreciated that any storage system supportingthe pool 1410 of storage resources can host the volumes 1430, 1432. Inthis example, volumes 1430, 1432 belong to a placement group 1434. Insome examples, the placement group 1434 belong to a particular tenantspace, which can span multiple storage systems. Volumes in a placementgroup may be aggregated based on specific policies assigned to thosevolumes. For example, a storage administrator can assign a policy thatdescribes an affinity between two volumes, such that those volumesbelong to the same placement group. Conversely, a storage administratorcan assign a policy that describes anti-affinity between two volumes,such that those volumes are placed in different placement groups. Asanother example, a placement group can be constructed of volumes forwhich data reduction, such as deduplication and compression, iscoordinated. As yet another example, a placement group can beconstructed based on utilization by a particular application such thatvolumes containing application data are included in the same placementgroup. As yet another example, a placement group can be constructed ofvolumes that share particular performance, permission, or protectioncharacteristics.

In some implementations, as depicted in FIG. 14, volumes in a placementgroup are localized to the same storage system. For example, FIG. 14shows that volumes 1430, 1432 in placement group 1434 reside together onstorage system 1412 in this particular example. In theseimplementations, all of the volumes in a placement group migratetogether. For example, where the placement group 1434 includes twovolumes 1430, 1432, volume 1430 would not migrate to storage system 1414without volume 1432 also migrating to storage system 1414. Thus, aplacement group may ensure that crash consistent snapshots are generatedfor all volumes in the placement group, for snapshot implementationswhere crash consistency is provided within a single storage system.

In other implementations, which will be described in more detail below,volumes in a placement group are constrained to a set of storagesystems, where that set of storage systems is a subset of the pluralityof storage systems that are utilized to provide the pool 1410 of storageresources. That is, in some examples, a placement group is associatedwith a limited number of storage systems on which volumes in theplacement group may reside. In these implementations, individual volumescan move between any these placement group storage systems. Hostrequests are directed to the storage system that currently hosts avolume, for example, through the use of asymmetric logical access (ALUA)or asymmetric namespace access (ANA) protocols, while still allowing ashared set of target ports for all volumes in the placement group. Thelimited set of storage systems that individual volumes can move betweenis updated together for the whole group. In such implementations, wherestorage systems implement cross-storage system consistent snapshots orcross-storage system consistency groups, consistent snapshots of aplacement group can be supported even when volumes within a placementgroup are hosted on separate storage systems associated with theplacement group, as will be described in more detail below.

Although the placement group 1434 is depicted as stored on the storagesystem 1412, in some examples, the placement group 1434 can bereplicated across storages storage systems. In such an example, thestorage systems 1412, 1414 may engage a replication relationship withrespect to these volumes. For example, the storage systems 1412, 1414can engage in a synchronous replication relationship, as discussedpreviously, in which both storage systems maintain an up-to-date copy ofthe volumes 1430, 1432. In such an example, the storage systems 1412,1414 can belong to the same pod. The storage systems 1412, 1414 can alsoengage in an asynchronous replication relationship in which updates tothe volume 1430, 1432 are replicated through checkpointing or periodicsnapshots, as discussed previously. In these examples, all volumes inthe placement group 1434 follow the same replication policies andmechanisms and should be limited at any one time to being stored withinstorage systems that can ensure consistent replication of all volumes inthe placement group.

To provide virtualization of a data path between the host 1422 and aparticular volume, the storage system orchestration service 1408 assignsa virtual namespace identifier to an individual volume 1430, 1432 or toa placement group 1434. The virtual namespace identifier can be, forexample, an iSCSI qualified name (IQN), NVMe-over-Fabrics qualified name(NQN), extended unique identifier (EUI), worldwide name (WWN), and soon, depending on a connectivity medium for a data path. The virtualnamespace identifier can be assigned without respect to the underlyingstorage system on which the volume or placement group is originallyprovisioned.

In some examples, mapping 1404 a virtual namespace identifier 1402 toone or more volumes 1430, 1432 stored among a pool 1410 of storageresources is carried out by the storage orchestration service 1408assigning a virtual namespace identifier to the placement group 1434that includes the one or more volumes 1430, 1432. In other examples,mapping 1404 a virtual namespace identifier 1402 to one or more volumes1430, 1432 stored among a pool 1410 of storage resources is carried outby the storage orchestration service 1408 assigning the virtualnamespace identifier 1402 to an individual volume such a volume 1430 orvolume 1432. As depicted in the particular example shown in FIG. 14, thevirtual namespace identifier is mapped to the placement group 1434. Insome examples, the virtual namespace identifier 1402 is assigned to oneor more volumes 1430, 1432 of the placement group 1434 when the volumes1430, 1432 are initially provisioned by the storage system orchestrator1408. In other examples, the virtual namespace identifier 1402 isassigned to the one or more volumes 1430, 1432 or the placement group1434 when a policy is assigned.

The method of FIG. 14 also includes migrating 1406 the first virtualnamespace identifier 1402 among the plurality of storage systems 1412,1414 to virtualize a data path 1450, 1452 for the one or more volumes1430, 1432. In some examples, migrating 1406 the first virtual namespaceidentifier 1402 is carried out by the storage orchestration service 1408directing the storage system that services a volume 1430, 1432 orplacement group 1434 to answer to the virtual namespace identifier 1402mapped to that volume or placement group. For example, the storageorchestration service 1408 can provide configuration information to thestorage system that configures the storage system to answer to thevirtual namespace identifier 1402. In some examples, migrating 1406 thefirst virtual namespace identifier 1402 is carried out in response to achange in the state of the data path 1450, 1452 to a volume 1430, 1432or placement group 1434. For example, where a volume migrates from onestorage system to another, or where host access to a dataset migratesfrom one storage system to another (e.g., during failover or loadrebalancing, or based on a change to a policy assigned to the placementgroup), the virtual namespace identifier 1402 is also migrated to theother storage system. Thus, in some examples, providing configurationinformation to configure a storage system to answer to a virtualnamespace identifier 1402 can be carried out automatically in responseto detecting that an event has occurred that affects the virtualizeddata path 1450, 1452 for the volume 1430, 1432 or placement group 1434.In these examples, the namespace identifier utilized 1402 by the host toestablish the session (i.e., the virtual namespace identifier 1402) doesnot change, and thus the reconfiguration of the host 1422 is notnecessary.

In some examples, the storage orchestration service 1408 migrates thevirtual namespace identifier 1402 for the placement group 1434 to thestorage system 1412 that services the volumes 1430, 1432 in thatplacement group. In some implementations, the virtual namespaceidentifier 1402 may be migrated from the storage orchestration service1408 to the storage system 1412 servicing the volumes 1430, 1432 duringa session login by redirecting the host 1422 using the virtual namespaceidentifier 1402, as will be discussed in more detail with respect toFIGS. 15 and 16. In other implementations, the storage systemorchestrator 1408 migrates the virtual namespace identifier bycoordinating the registration and deregistration of the virtualnamespace identifier 1402 by the storage systems 1412, 1414 providingthe pool 1410 of storage resources, as will be discussed in more detailwith respect to FIG. 17.

For further explanation, FIG. 15 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 15 is similar to the example method depicted in FIG. 14, as theexample method depicted in FIG. 15 also includes mapping 1404 a firstvirtual namespace identifier 1402 to one or more volumes 1430, 1432stored among a pool 1410 of storage resources, wherein a plurality ofstorage systems 1412, 1414 are utilized to provide the storageresources; and migrating 1406 the first virtual namespace identifier1402 among the plurality of storage systems 1412, 1414 to virtualize adata path 1450, 1452 for the one or more volumes 1430, 1432.

The example method of FIG. 15 also includes assigning 1502 the firstvirtual namespace identifier 1402 to a first storage system 1412. Afterthe storage orchestration service 1408 maps a virtual namespaceidentifier to a volume or a placement group, the storage orchestrationservice 1408 identifies the storage system that services the volume towhich the virtual namespace identifier is mapped, or that services thevolumes included in the placement group to which the virtual namespaceidentifier is mapped. The storage orchestration service 1408 thenconfigures that storage system to answer to the virtual namespaceidentifier. Thus, in some examples, the storage orchestration service1408 configures the storage system 1412 that services the volume 1430,1432 in the placement group 1434 to answer to the virtual namespaceidentifier 1402. Host access for the volumes 1430, 1432 using thevirtual namespace identifier 1402 is then redirected to the storagesystem that 1412 that services those volumes.

In the example method of FIG. 15, migrating 1406 the first virtualnamespace identifier 1402 among the plurality of storage systems 1412,1414 to virtualize a data path 1450, 1452 for the one or more volumes1430, 1432 includes reassigning 1504, in dependence upon a data pathevent, the first virtual namespace identifier 1402 to a second storagesystem 1414. In some examples a data path event is a state change to adata path. Generally, a data path event is a condition in which thevirtualized data path through which the host 1422 accesses the volumes1430, 1432 is migrated from one storage system to another. In a varietyof scenarios, the storage system utilized to provide storage resourcesto the host 1422 can change. For example, a volume or placement groupcan migrate from one storage system to another, necessitating amigration of a virtualized data path for host access to the volume orplacement group. The migration of a volume or placement group can be,for example, in response to a storage administrator configuring themigration. The migration of a volume or placement group can also be, forexample, initiated by the storage orchestration service 1408 inaccordance with a policy or change to a policy that has been assigned tothe volume or placement group. A data path event may also be theaddition of a new storage system to a set of placement group storagesystems, or the removal of a storage system from a set of placementgroup storage systems. Disaster recovery, load rebalancing, or otherevents can also necessitate the migration of a virtualized data path forhost access from one storage system to another storage system capable ofservicing the volume or placement group. Thus, where two storage systemsimplement a replication policy with respect to the volumes or placementgroup, the virtualized data path can be migrated between them. In someexamples, not all data paths for a volume or placement group arenecessarily migrated. In other words, where two storage systems arecapable of synchronously servicing a volume, some data paths may bemigrated to a second storage system while other data paths remain withthe first storage system.

Thus, the storage orchestration service 1408 can identify a data pathevent when initiating or detecting an event that necessitates a changein a data path between the host 1422 and the underlying storage systemthat services a volume or placement group. In the example depicted inFIG. 15, the storage orchestration service 1408 identifies, as a datapath event, that volumes 1430, 1432 in placement group 1434 are beingmigrated from the storage system 1412 to the second storage system 1414.For example, the storage orchestration service 1408 may initiate themigration of the placement group 1434 in accordance with one or morepolicies assigned to the placement group. Thus, the storageorchestration service identifies a state change for at least one datapath to the placement group 1434.

As such, the storage orchestration service 1408 configures the storagesystem 1414 to answer to the virtual namespace identifier 1402. Hostaccess for the volumes 1430, 1432 using the virtual namespace identifier1402 is then redirected to the storage system that 1412 that nowservices those volumes. For example, in response to identifying thatvolumes 1430, 1432 in placement group 1434 are being migrated or havebeen migrated from the first storage system 1412 to the second storagesystem 1412, the second storage system 1412 is configured to use thevirtual namespace identifier 1402 and a configuration request from ahost will be redirected to the second storage system 1414.

For further explanation, FIG. 16 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 16 is similar to the example method depicted in FIG. 15, as theexample method depicted in FIG. 16 also includes mapping 1404 a firstvirtual namespace identifier 1402 to one or more volumes 1430, 1432stored among a pool 1410 of storage resources, wherein a plurality ofstorage systems 1412, 1414 are utilized to provide the storageresources; assigning 1502 the first virtual namespace identifier 1402 toa first storage system 1412; and migrating 1406 the first virtualnamespace identifier 1402 among the plurality of storage systems 1412,1414 to virtualize a data path 1450, 1452 for the one or more volumes1430, 1432, including reassigning 1504, in dependence upon a data pathevent, the first virtual namespace identifier 1402 to a second storagesystem 1414.

In the example method of FIG. 16, assigning 1502 the first virtualnamespace identifier 1402 to a first storage system 1412 includes, inresponse to a discovery request 1640 from a host 1422, providing 1602 tothe host 1422 a discovery response 1642 that includes the virtualnamespace identifier 1402 and one or more first network addresses. Inthe example of FIG. 16, a virtual namespace identifier 1402 is migratedfrom the storage orchestration service 1408 to the storage system 1412during an initial session establishment with the host 1422. In someexamples, to mount any volume stored in the pool 1410 of storageresources, the host 1422 sends a discovery request message 1640 to anetwork address (e.g., an IP address) configured on the host 1422 fordiscovering storage available to that host. For example, the storageavailable to the host 1422 can be associated with a tenant space mappedto a host account. The discovery request message 1640 is received by adiscovery service of the storage orchestration service 1408. Thediscovery service answers to the network address, which can be, forexample, a virtual network address (e.g., a virtual IP address) that canbe floated among hardware configured to host an instance of the storageorchestration service 1408. The discovery request message 1640 includescredentials for the host 1422 and a request for available storage systemtargets. The discovery service of the storage orchestration service 1408maps the host credentials to volumes that are available to the host1422. In one example, the discovery service maps the host logininformation to a tenant space associated with the host 1422 andidentifies available volumes within that tenant space. The discoveryservice identifies mapping of a virtual namespace identifier 1402 toeach volume 1430, 1432 or placement group 1434. In the example depictedin FIG. 16, the discovery service identifies a mapping of a virtualnamespace identifier 1402 to the placement group 1434 that includesvolumes 1430. 1432, which are volumes that are available to the host1422.

In some examples, the discovery service of the storage orchestrationservice 1408 replies to the discovery request message 1640 with adiscovery response message 1642 that includes the virtual namespaceidentifiers 1402 associated with volumes 1430, 1432 or placement groups1434 that are available to the host 1422. In some examples, thediscovery response message 1642 includes one or more proxy networkaddresses (e.g., IP addresses) associated with the storage orchestrationservice 1408. The one or more proxy network addresses are answered bythe storage orchestration service 1408 rather than a storage system thatcurrently services the volumes or placement groups. That is, the one ormore proxy network addresses can be provided to allow the host 1422 toestablish a session using the virtual namespace identifier 1402. The oneor more proxy network addresses are a placeholder for one or morephysical network addresses of a storage controller 1412 that servicesthe volume 1430, 1432 or placement group 1434 associated with thevirtual namespace identifier 1402. In other words, for each proxynetwork address the host 1422 can be redirected to a physical networkaddress of a storage controller. In some examples, the one or more proxynetwork addresses are virtual network addresses (e.g., virtual IPaddresses) that can be floated to any storage system or other hardwareconfigured to host an instance of the storage orchestration service1408. That is, the storage orchestration service 1408 may beinstantiated on any of the storage systems 1412, 1414 in the pool 1410,or on a separate management server (not shown).

In some implementations, where iSCSI is the connectivity medium for datapaths, in response to a discovery request 1640 from a host 1422,providing 1602 to the host 1422 a discovery response 1642 that includesthe virtual namespace identifier 1402 and one or more first networkaddresses is carried out by the storage orchestration service 1408receiving a discovery request from the host 1422 that requests availabletargets. The host 1422 is configured with a virtual IP address of aniSCSI discovery service, which may be provided by the storageorchestration service 1408 or by other iSCSI endpoint, to initiate adiscovery session. In one example, the host 1422 issues a SendTargetsmessage (i.e., a discovery request) to this virtual IP address, which isanswered by the iSCSI discovery service. The iSCSI discovery serviceinspects the initiator IQN and identifies the volumes (e.g., volumes)that are available to the initiator IQN. The storage orchestrationservice 1408 provides the virtual IQNs (i.e., virtual namespaceidentifiers) mapped to volumes or placement groups that are available tothe host. The iSCSI discovery service returns a SendTargetsResponsemessage (i.e., a discovery response) to the host 1422, where theSendTargetsResponse includes virtual IQNs and virtual IP addresses(i.e., proxy network addresses) that will be used for redirection tophysical IP addresses of a storage controller. In the example depictedin in FIG. 14, an iSCSI discovery service provided by the storageorchestration service 1408 provides a SendTargetsResponse that includesa virtual IQN that maps to the placement group 1434 and virtual IPaddresses that are answered by an iSCSI target in the storageorchestration service 1408.

In the example method of FIG. 16, assigning 1502 the first virtualnamespace identifier 1402 to a first storage system 1412 also includes,in response to a first login request 1644 from the host 1422, providing1604 to the host 1422 a first redirection message 1646 that identifiesone or more second network addresses, wherein the one or more secondnetwork addresses are associated with the first storage system 1412 thatservices the one or more volumes 1430, 1432. In some examples, inresponse to receiving the discovery response message 1642, the host 1422sends a login request message 1644 directed to the one or more proxynetwork addresses in the discovery response message 1642. The loginrequest message 1644 is a request to establish a session using thevirtual namespace identifier 1402. The login request 1644 is received bythe storage orchestration service 1408, which answers to the proxynetwork addresses. In some examples, in response to receiving the loginrequest message 1644, the storage orchestration service 1408 sends aredirection message 1646 to the host 1422. The redirection message 1646indicates that the target corresponding the virtual namespace identifierhas changed. In these examples, the redirection message 1646 indicatesone or more target network addresses (e.g., IP addresses) correspondingto the storage system that services the volume or placement group towhich the virtual namespace identifier is mapped. In the exampledepicted in FIG. 16, where the login request message 1646 identifies avirtual namespace 1402 identifier mapped to the placement group 1434,the redirection message 1646 includes the physical network addresses ofthe storage system 1412 that services the volumes 1430, 1432 in theplacement group 1434.

As discussed above, where multipathing is enabled, multiple namespaceidentifiers may be provided for the same volume or placement group. Insuch examples, a login request may be received for each virtualnamespace identifier. Where a placement group is associated withmultiple storage systems, a login request is received for each virtualnamespace identifier corresponding to each storage system for theplacement group. Although only one of the storage systems stores aparticular volume, each of the storage systems for the placement groupadvertises all volumes in the placement group. Where a particular volumeamong the one or more volumes 1430, 1432 is symmetrically andsynchronously replicated across two or more storage systems 1412, 1414,each storage system that stores that particular volume can report anactive/optimized ALUA/ANA state for that volume.

In some examples, the host 1422 retries the login request using the samevirtual namespace identifier used in the login request 1644 but insteaddirected to the network addresses included in the redirection message1646. Thus, the virtual namespace identifier is migrated to theappropriate storage system. In the example depicted in FIG. 15, the host1422 retries the login request using the network addresses of thestorage system 1412 that services the placement group 1434. Uponcompleting the login, the host 1422 and storage system 1412 establish asession that includes virtualize data paths 1450, 1452 between the host1422 and the storage system 1412 using the virtual namespace identifierassociated with the placement group 1434.

In some implementations, where iSCSI is the connectivity medium for datapaths, providing, to the host 1422, the first redirection message 1646is carried out by the storage orchestration service 1408 receiving alogin protocol data unit (PDU) directed to the virtual IP addressesreturned in the SendTargetsResponse message and, in response, providinga redirection message indicating that the virtual IQN included in thelogin request has been moved. The redirect message includes the physicalIP addresses of the storage system that is currently servicing thevolume or placement group associated with the virtual IQN. Thus, allhosts wishing to connect to a particular set of volumes presented on apool of storage systems are provided a single set of target IP addressesfor discovery login, a single target IQN to address for those particularvolumes, and session login redirection to the storage system currentlyhosting the volume(s). At a high level, these techniques avoid the needfor host-configured drivers or agents by taking advantage of the iSCSIconnection protocol, orchestrating volume movement across storagesystems, and separating the iSCSI discovery target from the underlyingstorage systems.

In some examples, where the connectivity medium is NVMe-oF, the virtualnamespace identifier is a virtual NQN. Providing a discovery responsemessage that includes a virtual NQN and redirecting a host login usingthe virtual NQN can be carried out as discussed above with respect tothe iSCSI procedures.

In the example method of FIG. 16, reassigning 1504, in dependence upon adata path event, the first virtual namespace identifier 1402 to a secondstorage system 1414 includes, in response to a second login request 1648from the host 1422, providing 1606 to the host 1422 a second redirectionmessage 1650 identifying one or more third network addresses, whereinthe one or more third network addresses are associated a second storagesystem 1414. In some examples, in response to identifying the statechange for at least one data path to a volume or placement group, thephysical connection supporting that data path can be broken. Forexample, the storage orchestration service 1408 can direct the storagesystem 1412 to close a socket or otherwise break the connection to thehost 1422. In such examples, the host 1422 can retry the login bysending another login request message 1648 directed to the one or moreproxy network addresses in the discovery response message 1642. Theretry login request 1648 is received by the storage orchestrationservice 1408, which answers to the proxy network addresses. In theexample depicted in FIG. 16, the login request message 1648 is directedto virtual network addresses answered by the storage orchestrationservice 1408 to establish a session using a virtual namespace identifiermapped to the placement group 1434.

In some examples, in response to receiving the retry login requestmessage 1648, the storage orchestration service 1408 sends a redirectionmessage 1650 to the host 1422. The redirection message 1650 indicatesthat the target corresponding the virtual namespace identifier 1402 haschanged. In these examples, the redirection message 1650 indicates oneor more target network addresses (e.g., IP addresses) corresponding tothe storage system to which the virtual namespace identifier 1402 hasbeen migrated. In the example depicted in FIG. 16, where the loginrequest message 1650 identifies a virtual namespace identifier 1402mapped to the placement group 1434, the redirection message 1650includes the physical network addresses of the storage system 1414 towhich the placement group 1434 has migrated.

In some examples, the host 1422 retries the login request using the samevirtual namespace identifier used in the login request 1644 but insteaddirected to the network addresses included in the redirection message1650. Thus, the virtual namespace identifier is migrated to theappropriate storage system. In the example depicted in FIG. 16, the host1422 retries the login request using the network addresses of thestorage system 1414 that services the placement group 1434. Uponcompleting the login, the host 1422 and storage system 1414 establish asession that uses the virtualized data paths 1450, 152 between the host1422 and the storage system 1414 using the virtual namespace identifier1402 associated with the placement group 1434. For example, connectionssupporting the virtualized data paths 1450, 1452 are mapped to IPaddresses of a first storage controller of the storage system 1414.

In some implementations, where iSCSI is the connectivity medium for datapaths, providing 1606 to the host 1422 a second redirection message 1650identifying one or more third network addresses, wherein the one or morethird network addresses are associated a second storage system 1414, iscarried out by the storage orchestration service 1408 receiving a secondlogin protocol data unit (PDU) directed to the virtual IP addressesreturned in the SendTargetsResponse message and, in response, providinga redirection message indicating that the virtual IQN included in thelogin request has been moved. The redirect message includes the physicalIP addresses of the storage system that is currently servicing thevolume or placement group associated with the virtual IQN.

For further explanation, FIG. 17 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 17 is similar to the example method depicted in FIG. 15, as theexample method depicted in FIG. 17 also includes mapping 1404 a firstvirtual namespace identifier 1402 to one or more volumes 1430, 1432stored among a pool 1410 of storage resources, wherein a plurality ofstorage systems 1412, 1414 are utilized to provide the storageresources; assigning 1502 the first virtual namespace identifier 1402 toa first storage system 1412; and migrating 1406 the first virtualnamespace identifier 1402 among the plurality of storage systems 1412,1414 to virtualize a data path 1450, 1452 for the one or more volumes1430, 1432, including reassigning 1504, in dependence upon a data pathevent, the first virtual namespace identifier 1402 to a second storagesystem 1414.

The example method of FIG. 17 also includes mapping 1704 a secondvirtual namespace identifier 1702 to the one or more volumes 1430, 1432.In some cases, host multipath drivers allow the utilization of two ormore distinct virtual namespace identifiers for access to the samevolume. Thus, in some examples, the storage orchestration service 1408maps a second virtual namespace identifier 1702 to the same set of oneor more volumes 1430, 1432, or the same placement group 1434, to whichthe first virtual namespace identifier is mapped. In some examples, thestorage orchestration service 1408 maps 1704 the second namespaceidentifier 1702 in the same manner as mapping 1404 the first virtualnamespace identifier, as discussed above.

The example method of FIG. 17 also includes assigning 1706 the secondvirtual namespace identifier 1702 to the first storage system 1412. Insome examples, the storage orchestration service 1408 assigns the secondnamespace identifier 1702 to the first storage system 1412 in the samemanner as assigning 1502 the first namespace identifier 1402 to thefirst storage system 1412, as discussed above. However, in theseexamples, an asymmetric access mechanism, such as ALUA protocol foriSCSI or ANA protocol for NVMe, are utilized to report access states fora data path to a particular volume. That is, an access state can beassociated with a virtual namespace identifier with respect to accessinga particular volume or placement group. In some implementations, accessstates associated with a virtual namespace identifier may include anoptimized state, where a data path that uses the virtual namespaceidentifier is an active and preferred path for access to the volume; anon-optimized access state where a data path that uses the virtualnamespace identifier is an active path but not a preferred path foraccess to the volume; a standby state that indicates to a host that adifferent data path should be used for access to the volume (i.e., apath that uses a different virtual namespace identifier); or an inactivestate that indicates that no requests should be issued to a particularvolume using the virtual namespace identifier. For example, the accessstates can be the Active/Optimized, Active/Non-optimized, Standby, andInactive states used by ALUA and ANA protocols. Thus, host access to theone or more volumes 1430, 1432 can be directed to use a particular datapath based on these reported access states. There can be more than onepath to a volume in any one state. For example, where a storage systemcomprises multiple target ports that virtual namespace identifiers canbe mapped to or where a volume is symmetrically synchronously replicatedacross storage systems, each storage system storing that volume canreport an active/optimized state. As depicted in FIG. 17, both virtualnamespace identifiers 1402, 1702 are assigned to the first storagesystem 1412. In one example, one virtual namespace identifier may beassigned to a first controller of the storage system 1412 while a secondvirtual namespace identifier may be assigned to a second controller ofthe storage system. Thus, one data path 1450 to the one or more volumesmay utilize the first virtual namespace identifier 1402 while a seconddata path 1452 may utilize the second virtual namespace identifier 1702.In one example, both virtual namespace identifiers 1402, 1702 areassociated with an active optimized access state for a particularvolume, or one virtual namespace identifier 1402 is associated with anactive non-optimized state while the other 1702 is associated with anactive optimized state for a particular volume.

The method of FIG. 17 also includes directing 1708, utilizing anasymmetric access protocol, host access for the one or more volumes1430, 1432. In some examples, the storage orchestration service 1408migrates one virtual namespace identifier to a migration target storagesystem as part of a migration of a volume to that migration target,while the other virtual namespace identifier remains assigned to themigration source storage system. For example, the migration 1406 of thevirtual namespace identifier can be performed prior to initiating orduring the migration of the volume to test a connection between a hostand the migration target before migration of the volume is competed. Insome examples, until the migration completes successfully, the accessstate associated with the virtual namespace identifier assigned to themigration source storage system is reported as active/optimized, whilethe access state associated with the virtual namespace identifierassigned to the migration target storage system is reported asactive/non-optimized, standby, or inactive. After the migration of thevolume completes successfully, the access state associated with thevirtual namespace identifier assigned to the migration source storagesystem is reported as active/non-optimized, standby, or inactive, whilethe access state associated with the virtual namespace identifierassigned to the migration target storage system is reported asactive/optimized. It should be recognized that a storage system assignedwith a virtual namespace identifier that is in the active/non-optimizedstate or standby state may, in some examples, forward a received accessrequest for the volume to storage system that is currently servicingthat volume.

In the example depicted in FIG. 17, the storage orchestration servicecan migrate the first virtual namespace identifier 1402 to the secondstorage system 1414 as part of a migration of the one or more volumes1430, 1432 to the second storage system 1414. For example, prior toinitiating or during the migration of a volume, the storageorchestration service 1408 can migrate the first virtual namespaceidentifier 1402 to the second storage system and prompt the host 1422 tologin to the second storage system 1414. Thus, the data path 1450 thatutilizes the first virtual namespace identifier 1402 for a migrationvolume is reestablished with the second storage system 1414. In someexamples, an access state associated with the data path 1450 is reportedas active/non-optimized, standby, or inactive until the migration of theone or more volumes 1430, 1432 completes, while the access stateassociated with the data path 1452 (corresponding to the second virtualnamespace identifier 1702) is reported as active/optimized. In theseexamples, after the migration of the one or more volumes 1430, 1432 iscomplete, the second storage system 1414 assigned with the first virtualnamespace identifier 1402 transitions to an active/optimized state whilethe first storage system assigned with the second virtual namespaceidentifier 1702 transitions to an active/non-optimized, standby, orinactive state. The access states may be reported by the storage systems1412, 1414 over the targets ports of those storage systems 1412, 1414mapped by the by the virtual namespace identifiers.

For further explanation, FIG. 18 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 18 is similar to the example method depicted in FIG. 14, as theexample method depicted in FIG. 18 also includes mapping 1404 a firstvirtual namespace identifier 1402 to one or more volumes 1430, 1432stored among a pool 1410 of storage resources, wherein a plurality ofstorage systems 1412, 1414 are utilized to provide the storageresources; and migrating 1406 the first virtual namespace identifier1402 among the plurality of storage systems 1412, 1414 to virtualize adata path 1450, 1452 for the one or more volumes 1430, 1432.

The example method of FIG. 18 also includes mapping 1804 one or moreadditional virtual namespace identifiers 1802 to the placement group. Asmentioned above, mapping 1404 the virtual namespace identifier to theone or more volumes can include mapping the virtual namespace identifierto a placement group that includes the one or more volumes, where theone or more volumes in the placement group are constrained to a set ofplacement group storage systems. For example, in a particular exampledepicted in FIG. 18, the placement group 1434 is associated with a set1814 of placement group storage systems 1412, 1812 (where storage system1812 is also included the plurality of storage systems that are utilizedto provide the pool 1410 of storage resources). Thus, the location ofthe volumes 1430, 1432 are limited to the storage systems 1412, 1812,and individual volumes 1430, 1432 can be located on and moved betweeneither storage systems 1412, 1812. In some examples, all of the storagesystems 1412, 1812 in the set 1814 of placement group storage systemsadvertise all of the volumes 1430, 1432 in the placement group 1434,even though only one storage system stores a particular volume in theplacement group 1434. In one example, as depicted, one volume 1430 islocated on one storage system 1412 while another volume 1432 in theplacement group 1434 is located on another storage system 1812, whileboth storage systems 1412, 1812 advertise both volumes 1430, 1432. Itshould be recognized that a set 1814 of placement group storage systemsis not limited to only two storage systems and may include more than twostorage systems. Similarly, a placement group 1434 is not limited to twovolumes and may include more than two volumes. In some examples, thestorage orchestration service 1408 maps 1804 the one or more additionalvirtual namespace identifiers in a manner similar to mapping more thanone virtual namespace identifier to a placement group, as discussedabove.

In some implementations, the one or more additional virtual namespaceidentifiers 1802 are mapped such that the total number of virtualnamespace identifiers 1402, 1802 mapped to the placement group 1434 isequal to or greater than the number of storage systems in the set 1814of placement group storage systems. The number of virtual namespaceidentifiers mapped to the placement group 1434 may be selected to allowmore than one virtual namespace identifier to be assigned to a storagesystem, as discussed above. In some examples, the number of virtualnamespace identifiers that will be assigned to a storage system is basedon a number of target ports on that storage system. Where a storagesystem is a dual controller storage system, the number of virtualnamespace identifiers that will be assigned to a storage system may bebased on the number of controllers and the number or ports on eachcontroller. For example, in a dual controller storage system with twotarget ports per controller, four virtual namespace identifiers would beassigned to a storage system. Consider an example where a placementgroup 1434 includes three storage systems that are dual controllerstorage systems with two ports per controller. In an exampleimplementation where one virtual namespace identifier is assigned toeach port of each placement group storage system, twelve virtualnamespace identifier is a minimum number of virtual namespaceidentifiers that should be mapped to the placement group 1434 in thisparticular example. However, additional namespace identifiers can bemapped to the placement group 1434 in anticipation of the addition of astorage system to the placement group storage systems, as will bediscussed below. For example, a particular storage system may beassigned duplicate virtual namespace identifiers, i.e., two virtualnamespace identifiers per port per controller, in one exampleimplementation. In such an example, sixteen virtual namespaceidentifiers should be mapped to the placement group 1434. However, inthe examples discussed below, for the sake of clarity, an exampleimplementation is provided where a minimum of one virtual namespaceidentifier is assigned to one port of a storage system as a minimumimplementation for providing access to the one or more volumes on eachplacement group storage system. When a storage system is added to aplacement group and another storage system is removed, and when theminimum number of virtual namespace identifiers have been mapped to theplacement group (e.g., N virtual namespace identifiers for N ports amongM storage systems) the virtual namespace identifier of the system beingremoved can be migrated to the new storage system. However, where morethan the minimum number of virtual namespace identifiers have beenmapped to the placement group (e.g., (e.g., N+1 virtual namespaceidentifiers for N ports among M storage systems), an extra virtualnamespace identifier assigned to one of the placement group storagesystems can be migrated to the storage system being added.

The example method of FIG. 18 also includes assigning 1806 the firstvirtual namespace 1402 identifier and each of the one or more additionalvirtual namespace identifiers 1802 to a respective storage system 1412,1812 in the set 1814 of storage systems 1412, 1812 associated with theplacement group 1434. In some examples, the storage orchestrationservice 1408 assigns one (or at least one) virtual namespace identifier1402, 1802 to each placement group storage system 1412, 1812. Asdiscussed above, in some implementations, the storage orchestrationservice 1408 assigns more than one virtual namespace identifier to oneor more of the placement group storage systems 1412, 1812. For example,storage system 1412 may be assigned both the first virtual namespaceidentifier 1402 and the second virtual namespace identifier 1702(discussed above and shown in FIG. 17), while storage system 1812 isassigned one virtual namespace identifier 1802. In these examples, allof the virtual namespace identifiers 1402, 1702, 1802 are mapped to theplacement group 1434.

The example of method of FIG. 18 also includes directing 1808, utilizingan asymmetric access protocol, host access for a particular volume amongthe one or more volumes 1430, 1432. As discussed above, in someexamples, an asymmetric access protocol such as ALUA or ANA is utilizedto direct host access to a volume, where a virtual namespace identifieris reported as a preferred target for access to the volume over othervirtual namespace identifiers also associated with the volume. Thus,where multiple virtual namespace identifiers are mapped to placementgroup, and at least one of those virtual namespace identifiers isassigned to each placement group storage system, asymmetric accessstates for those virtual namespace identifiers can direct the host tothe placement group storage system that currently stores a particularvolume. As discussed above, in some examples the access states includeactive/optimized, active/non-optimized, standby, and inactive withrespect to a volume. Where a volume is mirrored between two or moreplacement group storage systems within the placement group (such asthrough symmetric synchronous replication), more than one of theplacement group storage systems hosting the replicated volume can beconsidered active/optimized for that volume. Consider an example wheretwo storage systems 1412, 1812 in the set 1814 of placement groupstorage systems synchronously replicate a volume 1430. Each storagesystem 1412, 1812 can report an active/optimized state for the volume1430 through the virtual namespace identifiers 1402, 1802 assigned tothose storage systems.

In some examples, as depicted in the example of FIG. 18, all of theplacement group storage systems 1412, 1812 advertise all volumes 1430,1432 of the placement group 1434. In some examples, the access state forthe data path 1450 between the host 1422 and the virtual namespaceidentifier 1402 is reported as active/optimized to indicate that it isthe preferred data path for access to volume 1430. This is because thevirtual namespace identifier 1402 is assigned to the storage system 1412that stores the volume 1430. In some implementation, a second virtualnamespace identifier 1702 is also assigned to storage system 1412. Inthese implementations, the second virtual namespace identifier can beassociated with an active/optimized, active/non-optimized, or standbystate with respect to volume 1430. In some examples, for access tovolume 1432, an access state associated with the virtual namespaceidentifier 1802 is reported as active/optimized, as the storage system1812 stores that volume 1432. Thus, data path 1850 is the preferred datapath for access to volume 1432. In these examples, the first virtualnamespace identifier 1402 can be associated with an active/non-optimizedor standby state with respect to volume 1432, and the additional virtualnamespace identifier 1802 can be associated with an active/non-optimizedor standby state with respect to volume 1430. In some examples, accessrequests for volume 1430 received by the storage system 1812 areforwarded to the storage system 1412, and access requests for volume1432 received by the storage system 1412 are forwarded to the storagesystem 1812.

In some examples, storage systems can be added to or removed from theset 1814 of placement group storage systems by migrating volumes awayfrom a storage system that is being removed and/or migrating a virtualnamespace identifier mapped to the placement group 1434 to a new storagesystem being added. As discussed above, in some examples, one namespaceidentifier is mapped to each storage system for the placement group, andone or more duplicate namespace identifiers (two namespace identifiersfor the same storage system) are also provided. Thus, volumes may moveamong the storage systems for the placement group using simple ALUA/ANAstate changes, while the duplicate namespace identifiers may be usedwhen adding a new storage system for the placement group. When a newstorage system is added, one of the virtual namespace identifiers ismigrated to the new storage system. When a storage system is removed,one of the virtual namespace identifiers is migrated from that storagesystem to one of the storage systems remaining in the placement group.Volumes within the placement group would be migrated accordingly to makeuse of new storage systems and to be migrated from storage systems beingremoved. ALUA/ANA path management can be used to ensure that hosts favor(or exclusively use) only those virtual namespace identifiers that mapto a storage system that currently hosts any particular volume. Forexamples, the volumes may be adjusted to avoid use of a particularvirtual namespace identifier, that virtual namespace identifier may thenbe migrated to a new storage system, one or more volumes may then bemigrated to the new storage system, and then the ALUA/ANA states may beadjusted to favor use of the new storage system for the migratedvolumes. When removing a storage system from a placement group, thesequence may include migrating volumes first, then adjusting ALUAstates, and then migrating the virtual namespace identifiers previouslyassigned for the placement group to the now removed storage system.

Consider an example where storage system 1414 is being added to the set1814 of placement group storage systems and storage system 1412 is beingremoved. In such an example, the volumes (e.g., volume 1430) on storagesystem 1412 are migrated to storage system 1812. In someimplementations, the access state for the virtual namespace identifier1802 is transitioned to an/active optimized for those volumes that aremigrated. In other implementations, a virtual namespace identifier(e.g., virtual namespace identifier 1702) is also migrated to thestorage system 1812 and remains active/optimized for the migratedvolumes. A virtual namespace identifier (e.g., virtual namespaceidentifier 1402) assigned to storage system 1412 is transitioned toactive/non-optimized, standby, or inactive for the migrated volumes. Toadd the new storage system 1414 to the set 1814 of placement groupstorage systems, the host 1422 is prompted to login to storage system1414 and a virtual namespace identifier (e.g., virtual namespaceidentifier 1402) is migrated to the storage system 1414. Upon migrationof one of the virtual namespace identifiers (that are mapped to theplacement group 1434) to the new storage system 1414, the access statesfor the data path to the new storage system 1414 are reported asactive/non-optimized or standby with respect to the volumes 1430, 1432in the placement group 1434. However, volumes 1430, 1432 in theplacement group 1434 can now be migrated to the new storage system 1414,and the host 1422 may be directed to the new storage system 1414 byupdating the access state associated with the virtual namespaceidentifier 1402 now assigned to the new storage system 1414.

For further explanation, FIG. 19 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 19 is similar to the example method depicted in FIG. 18, as theexample method depicted in FIG. 19 also mapping 1404 a first virtualnamespace identifier 1402 to one or more volumes 1430, 1432 stored amonga pool 1410 of storage resources, wherein a plurality of storage systems1412, 1414 are utilized to provide the storage resources; mapping 1804one or more additional virtual namespace identifiers to the placementgroup; assigning 1806 the first virtual namespace 1402 identifier andeach of the one or more additional virtual namespace identifiers 1802 toa respective storage system 1412, 1812 in the set 1814 of storagesystems 1412, 1812 associated with the placement group 1434; directing1808, utilizing an asymmetric access protocol, host access to aparticular storage system hosting a particular volume among the one ormore volumes 1430, 1432; and migrating 1406 the first virtual namespaceidentifier 1402 among the plurality of storage systems 1412, 1414 tovirtualize a data path 1450, 1452 for the one or more volumes 1430,1432.

The example of FIG. 19 also includes migrating 1902 a first volume fromthe first storage system 1412 assigned with the first virtual namespaceidentifier 1402 to a third storage system 1812 assigned with a thirdvirtual namespace identifier 1802, wherein an asymmetric access statefor the first volume 1430 that is reported for the third virtualnamespace identifier 1802 is updated to indicate a preferred target, andwherein an asymmetric access state for the first volume 1430 that isreported for the first virtual namespace identifier 1402 is updated toindicate a non-preferred target. In some examples, if volume 1430 ismigrated (e.g., at the direction of the storage orchestrator 1408) fromstorage system 1412 to storage system 1812, the access state associatedwith the virtual namespace identifier 1802 is transitioned toactive/optimized with respect to volume 1430, and the virtual namespaceidentifier 1402 is transitioned to an active/non-optimized or standbystate with respect to volume 1430. Thus, storage system 1812 is nowindicated as the preferred target for volume 1430, and the preferredvirtualized data path 1450 for volume 1430 will move from storage system1412 to storage system 1812. Likewise, if volume 1432 is migrated fromstorage system 1812 to storage system 1412, the access state associatedwith the first virtual namespace identifier 1402 is transitioned toactive/optimized with respect to volume 1432, and the virtual namespaceidentifier 1802 is transitioned to an active/non-optimized or standbystate with respect to volume 1432. Thus, within the set 1814 ofplacement group storage systems 1412, 1812, volumes 1430, 1432 and hostaccess to those volumes may freely migrate by updating the access statesassociated with the virtual namespace identifiers assigned to theplacement group storage systems.

For further explanation, FIG. 20 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 20 is similar to the example method depicted in FIG. 18, as theexample method depicted in FIG. 20 also mapping 1404 a first virtualnamespace identifier 1402 to one or more volumes 1430, 1432 stored amonga pool 1410 of storage resources, wherein a plurality of storage systems1412, 1414 are utilized to provide the storage resources; mapping 1804one or more additional virtual namespace identifiers to the placementgroup; assigning 1806 the first virtual namespace identifier 1402 andeach of the one or more additional virtual namespace identifiers 1802 toa respective storage system 1412, 1812 in the set 1814 of storagesystems 1412, 1812 associated with the placement group 1434; directing1808, utilizing an asymmetric access protocol, host access to aparticular volume among the one or more volumes 1430, 1432; andmigrating 1406 the first virtual namespace identifier 1402 among theplurality of storage systems 1412, 1414 to virtualize a data path 1450,1452 for the one or more volumes 1430, 1432.

The method of FIG. 20 also includes coordinating 2002 consistentsnapshots 2030, 2032 for the one or more volumes 1430,1432 in theplacement group 1434 across the set 1814 of storage systems 1412, 1812associated with the placement group. As mentioned above, where storagesystems implement cross-storage system consistent snapshots orcross-storage system consistency groups, consistent snapshots of aplacement group can be supported even when volumes within a placementgroup are hosted on separate storage systems associated with theplacement group. In some examples, to generate consistent snapshots forthe placement group 1434, the storage orchestrator 1408 directs eachstorage system 1412, 1812 in the set 1814 of placement group storagesystems to generate respective snapshots 2030, 2032 for the respectivevolumes 1430, 1432 of the placement group that are stored on thosestorage systems. In some examples, directing the set 1814 of placementgroup storage systems to generate consistent snapshots 2030, 2032 forthe volumes 1430, 1432 can include directing the storage systems 1414,1812 to quiesce certain aspects of storage operation processing and togenerate the respective snapshots 2030, 2032. In such an example, theconsistent snapshots do not need to be established at the same point intime. The set of coordinated snapshots 2030, 2032 represents a versionof the volumes 1430, 1432 in the placement group in which each snapshot2030, 2032 does not include any updates to the volumes 1430, 1432 thatcould causally depend on any other update to the volumes 1430, 1432 thatwere excluded by another snapshot. In other words, snapshot 2030 doesnot include any updates to volume 1430 that rely on results of an updateto volume 1432 that were excluded from snapshot 2032, and vice versa. Assuch, the set of all consistent snapshots 2030, 2032 includes allupdates to the volumes 1430, 1432, across all placement group storagesystems 1412, 1812, upon which any included update could have depended.In some implementations, storage systems 1412, 1812 indicate that an I/Orequest has been fulfilled either by returning a read result, in thecase of a read request, or by providing a completion acknowledgement, inthe case of a write request, once the requested modification has beenpersisted. Until the host receives the completion acknowledgement, thehost cannot rely on the requested modification having been applied. Ifthe modification has not been signaled as complete, the result is notobservable such that it can be relied upon by other operations. Thus, asecond update can depend on a first update if the first update, or anoverlapping read that could have retrieved the results of the firstupdate, was signaled as completed prior to the second update beingreceived. In some examples, to coordinate the consistent snapshots 2030,2032, the storage orchestration service 1408 requests that the storagesystems 1412, 1414 prepare for a snapshot by suspending or quiescingsome part of I/O processing for the placement group volumes 1430, 1432.Once all placement group storage systems that are currently hostingvolumes of the placement group 1434 have been suspended or quiesced, thestorage orchestration service 1408 directs the storage systems 1412,1812 to apply a snapshot, thus resulting in consistent snapshots 2030,2032.

For further explanation, FIG. 19 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 19 is similar to the example method depicted in FIG. 14, as theexample method depicted in FIG. 19 also includes mapping 1404 a virtualnamespace identifier 1402 to one or more volumes 1430, 1432 stored amonga pool 1410 of storage resources, wherein a plurality of storage systems1412, 1414 are utilized to provide the storage resources; and migrating1406 the first virtual namespace identifier 1402 among the plurality ofstorage systems 1412, 1414 to virtualize a data path 1450, 1452 for theone or more volumes 1430, 1432.

In the example method depicted in FIG. 21, migrating 1406 the firstvirtual namespace identifier 1402 among the plurality of storage systems1412, 1414 to virtualize a data path 1450, 1452 for the one or morevolumes 1430, 1432 includes, in response to identifying a data pathevent, directing 2102 the first storage system 1412 to deregister thevirtual namespace identifier 1402 and directing the second storagesystem 1412 to register the virtual namespace identifier 1402. In someexamples, a storage system registers a virtual namespace identifier 1402with a registration service 2104. The virtual namespace identifier 1402maps to a placement group including volumes serviced by the storagesystem. The host 1422 uses the virtual namespace identifier providedthrough the registration service to establish a data path to the storagesystem that services the volumes in the placement group. Where FibreChannel is used as the connectivity medium for the data path, forexample, the virtual namespace identifier may be a virtual WWN and theregistration service may be a controller or name service in a FibreChannel switch (e.g., an N_port controller). Where iSCSI is used as theconnectivity medium for the data path, for example, the virtualnamespace identifier may be a virtual IQN and the registration servicemay be an internet storage name server (iSNS). The virtual namespaceidentifier 1402 can be provided to the storage system by the storageorchestration service 1408. In response to identifying a data pathevent, as discussed above, the storage orchestration services cancoordinate the migration of the virtual namespace identifier 1402 fromone storage system to another. As depicted in FIG. 21, the migration ofthe placement group 1434 from the storage system 1412 to the storagesystem 1414 is an event that precipitates a migration of the virtualizeddata path 1450, 1452 between the host 1422 and the volumes 1430, 1432 inthe placement group 1434.

In the example depicted in FIG. 21, in response to identifying a datapath event, directing 2102 the first storage system 1412 to deregisterthe virtual namespace identifier 1402 and directing the second storagesystem 1412 to register the virtual namespace identifier 1402 is carriedout by the storage orchestration service 1408 directing the storgesystem 1412 to deregister the virtual namespace identifier 1402corresponding to the placement group 1434 from the registration service,thus dissolving the data path(s) between the host 1422 and the storagesystem 1412. Further, the storage orchestration service 1408 directs thestorage system 1414 to register the virtual namespace identifier 1402corresponding to the placement group 1434 with the registration service.When the storage system 1412 deregisters from the registration service,the registration service raises a state change notification even to thehost 1422. The host 1422, upon detecting the state change notification,will retry a login to connect to the virtual namespace identifier 1402.Upon successful reestablishment, virtual namespace identifier 1402 forthe placement group 1434 has been migrated to the storage system 1414.The new data path 1452 is established for providing host access to thevolumes 1430, 1432 in the placement group.

For further explanation, FIG. 22 sets forth a flow chart illustrating anadditional example method of data path virtualization according to someembodiments of the present disclosure. The example method depicted inFIG. 14 is similar to the example method depicted in FIG. 14, as theexample method depicted in FIG. 22 also includes mapping 1404 a virtualnamespace identifier 1402 to one or more volumes 1430, 1432 stored amonga pool 1410 of storage resources, wherein a plurality of storage systems1412, 1414 are utilized to provide the storage resources; and migrating1406 the first virtual namespace identifier 1402 among the plurality ofstorage systems 1412, 1414 to virtualize a data path 1450, 1452 for theone or more volumes 1430, 1432.

The example method of FIG. 22 also includes presenting 2202, to the host1422, the pool 1410 of storage resources as a single storage system. Insome examples, presenting 2202, to the host 1422, the pool 1410 ofstorage resources as a single storage system is carried out by thestorage system orchestrator 1408 as discussed above. Using theabove-described infrastructure, presenting the pool 1410 of storageresources as single storage system by the storage orchestration service1408 can offer a number of features and services to both consumers ofstorage and storage providers.

In some examples, the use of iSCSI abstraction as a control point can beused to facilitate filtering, access control list operations, andsecurity operations. For containerized storage services, this providesenhance security compared to having a dedicated IP address. Through theuse of policies, the storage orchestration service 1408 may ensure thata modified policy is propagated to the storage system and that thestorage system ultimately converges on a desired state. In someexamples, using a virtual namespace identifier, the storageorchestration service 1408 can implement an object storage endpoint tofederate object storage namespaces across multiple storage devices.

In some examples, the storage orchestration service 1408 can providemulti-array management through a single framework rather than managingindividual storage arrays. Such storage management services that canmanage multiple arrays as data engine components within pools of storagethrough a single management framework. In some examples, the storageorchestration service 1408 provides an abstraction of users away fromthe physical infrastructure components that enables consumers to haveaccess to storage services while not having management access toback-end storage arrays that house the storage resources.

In some examples, the storage orchestration service 1408 can exposestorage services to multiple consumers or application owners (e.g.,tenants) from the same shared back-end infrastructure or pool of storageresources while segregating different groups of users from each other.Through the storage orchestration service 1408, storage may beprovisioned into a tenant space. A tenant space may be analogous to aworking environment or application. Tenant spaces can live within alarger tenant object. In these examples, users can have access to theresources within their own tenants or tenant spaces.

In some examples, the storage orchestration service 1408 can exposemultiple different performance capabilities (storage classes) from thesame underlying back-end infrastructure. The storage orchestrationservice 1408 may facilitate the presentation of storage classes withdefined properties limiting capacity and/or performance. On the storagesystem, volumes can be provisioned with QoS, IOPS, and bandwidth ratelimits to enforce the performance characteristics.

In some examples, the storage orchestration service 1408 can be used tolifecycle the back-end hardware in the environment without disruption.Redistributing storage resources across back-end arrays with zerodisruption to existing workloads allows for the adjustment ofutilization after initial placement of unpredictable workloads. Thisincludes environments where cooperative maintenance windows cannot becoordinated with the application owners. In some examples, the storageorchestration service 1408 can non-disruptively move volumes (in groups)between different arrays for the purpose of rebalancing workloads.Additionally, the storage systems themselves already have the ability tonon-disruptively update across hardware generations and to increase anddecrease capacity non-disruptively.

In some examples, the storage orchestration service 1408 can exposeresources as pools or availability zones rather than as individualarrays. Users will consume storage with the ability to specify affinityand anti-affinity for the underlying hardware. Regions and availabilityzones allow users to understand the hardware layout, and designapplications that can sustain hardware failures.

In some examples, the storage orchestration service 1408 can provide aninterface for storage consumers to consume storage with differentproperties for performance and or capacity, and to associate dataprotection policies with the storage objects they consume. In suchexamples, consumers may be able to provision volumes by instantiatingthem from storage classes. For example, the storage classes define theperformance characteristics of the volumes they create. Additionally,data protection features are provided as policies that consumers canassociate with the volumes as they are provisioned. In some examples,the storage orchestration service 1408 can provide an interface forstorage consumers to attach data protection policies to the resourcesthey consume. This includes snapshots and cross availability zone orcross region replication. In some examples, the storage orchestrationservice 1408 can expose data protection policies that can be attached tostorage as the objects are consumed by the user.

In some examples, the storage orchestration service 1408 can facilitatethe rebalance or movement of workloads between storage systems in thesame availability zone without disrupting the applications or requiringassistance from network administrators or host administrators. Theability to non-disruptively migrate workloads between different back-endhardware platforms is advantageous to storage management because itprovides the ability to absorb utilization changes in environments whereperformance requirements can be very unpredictable. In some examples,the storage orchestration service 1408 provides unassisted rebalance,where storage can be rebalanced by the storage orchestration service1408 without the need for the storage administrator coordinating withanother admin (e.g., there is no need to change the switchconfiguration, no need for host to rescan or change configuration,etc.). In some examples, the storage orchestration service 1408 providesassisted rebalance, where storage can be rebalanced by the storageorchestration service 1408 but with assistance or coordination withanother admin (e.g., zone/network changes, server rescans, etc.). Insome examples, the storage orchestration service 1408 provides automaticrebalance, in which the storage orchestration service 1408 decides whatto move and when/where to move it, and performs the rebalance with nohuman intervention.

Enterprise storage systems, or more particularly a fleet of enterprisestorage systems, typically provide telemetry data to a cloud-basedservice. For example, the telemetry data reported may include datadescribing various operating characteristics of the storage system andmay be analyzed by the cloud-based service for a vast array of purposesincluding, for example, to determine the health of the storage system,to identify workloads that are executing on the storage system, topredict when the storage system will run out of various resources, torecommend configuration changes, workflow migrations, or other actionsthat may improve the operation of the storage system. However, suchcloud-based services do not support a control channel for remote APIrequests directed to storage system APIs or for active management ofsoftware on the storage system. In the following examples, an edgemanagement service provides such a control channel between a cloud-basedservice (i.e., a cloud-based storage service) and an edge device (e.g.,a storage system controller or a server coupled to a storage system).

FIG. 23 sets forth a diagram of an example architecture for an edgemanagement service that provides a messaging channel between cloud-basedcomponents (such as cloud-based services) and enterprise devices (suchas edge servers or enterprise storage systems). The example of FIG. 23includes an edge device 2302 that is configured to receive controlmessages from a cloud-based storage service 2304 over a datacommunication link 2306. In some examples, the cloud-based storageservice 2304 is a storage service or set of storage services (e.g.,microservices) whose execution is supported by cloud resources (e.g.,cloud computing instances) of a cloud computing environment. Forexample, the cloud computing environment may include a public cloudenvironment, a private cloud environment, a virtual private cloudenvironment, a hybrid cloud environment, and so on.

In some examples, the edge device 2302 is a component of storage systemsuch as any of the storage systems discussed above. For example, edgedevice 2302 may be a component of an on-premises storage system locatedin an enterprise data center, a hosted storage system located in aremote or colocation data center, or some other physical storage system.In other examples, the edge device is 2302 is an enterprise server orother network appliance configured to interface with one or more storagesystems (e.g., a fleet of storage systems). In these examples, the edgedevice 2302 may be, for example, a server that is collocated with one ormore storage systems in an enterprise data center. In yet otherexamples, the edge device 2302 is a virtual device such a virtualmachine hosted on a physical storage system, server, or other devicecollocated with one or more enterprise storage systems. For ease ofexplanation, in the example of FIG. 23 and following examples, the edgedevice 2302 is described in the context of a on-premises physicalstorage system unless otherwise noted. For example, the edge device canbe embodied in a storage controller 2312 of a storage system 2362. Theedge device 2302 may also be configured to send telemetry data (e.g.,capacity, load, etc.) to the cloud-based storage service 2304.

In some examples, the cloud-based storage service 2304 includes an edgemanagement service 2350 for managing the edge device 2302 and directingstorage and data services on the edge device 2302. In this regard, thecloud-based storage service 2304 be associated with a user interface2360 for administrators and other enterprise personnel to configure theedge device 2302 and request storage and data services on the edgedevice 2302. For example, the user interface may be a web-based GUI. Insome examples, the cloud-based storage service 2304 includes acloud-based control plane 2346 for deploying and managing software onthe edge device 2302 as well as providing storage and data services tohosts utilizing the edge device 2302. In some examples, the cloud-basedstorage service 2304 includes one or more storage microservices 2340,2342, 2344 that provide storage and data services through a controlchannel to the edge device 2302. For example, the storage microservicesmay include a storage orchestration microservice 2342 that autonomouslyprovisions volumes or other datasets on the edge device 2302, migratesvolumes, scales out storage, and so on. As another example, the storagemicroservices may include a data protection as-a-service (DPaaS)microservice 2344 that manages snapshotting, RPO, RTO, and other dataprotection policies on the edge device 2302 based on customer-drivenobjectives. As another example, the microservices may include a softwareupdate microservice 2340 for deploying and upgrading software on theedge device 2302. As yet another example, the storage microservices mayinclude a disaster recovery as-a-service (DRaaS) microservice thatmanages data replication policies, failover procedures, and otherdisaster recovery safeguards for the edge device 2302. The cloud-basedstorage service 2304 may also provide other cloud services such as thosediscussed above in the context of FIG. 3A. In some non-limitingexamples, control messages sent to the edge device 2302 from thecloud-based storage service 2304 may include messages directing the edgedevice 2302 to install or update software on the edge device 2302,provision a volume on the edge device 2302, take a snapshot of a volumeon the edge device 2302, set a policy on the edge device 2302 and so on.In some examples, the control plane and its constituent microservicesare architected as a service mesh.

The data communications link 2306 may be embodied as a datacommunications pathway that is provided through the use of one or moreprivate and/or public data communications networks such as a LAN, WAN,SAN, the public Internet, a virtual private network, or as some othermechanism capable of transporting digital information between the edgedevice 2302 and the cloud-based storage service 2304. In such anexample, digital information, such as control messages and telemetrydata, may be exchanged between the edge device 2302 and the cloud-basestorage service 2304 via the data communications link 2306 using one ormore data communications protocols. For example, digital information maybe exchanged using hypertext transfer protocol (HTTP), internet protocol(IP), transmission control protocol (TCP), user datagram protocol (UDP),transport layer security (TLS), and so on. In some examples, the datacommunications link 2306 utilizes an Internet-of-Things (IoT) messagingprotocol such as the message queue telemetry transport (MQTT), advancedmessage queue protocol (AMQP), or other IoT messaging protocol. In suchexamples, messages and data may be passed between the edge device 2302and the cloud-based storage service 2304 via a publish/subscribemessaging model and a message broker. In some examples, the IoTmessaging layer supports over-the-air (OTA) installation and update ofsoftware on the edge device 2302 from the cloud-based storage service2304.

In some implementations, the edge device 2302 includes an edgemanagement service (EMS) client 2320 configured for communication withthe edge management service 2350 of the cloud-based storage service2304. The EMS client 2320 receives control messages published by theedge management service 2350, including but not limited to softwareinstallation messages, software update messages, security patching, andAPI calls for storage orchestration, DPaaS, DRaaS, and other storage anddata services. As such, the EMS client 2320 may read control messagespublished by the control plane 2346 including the storage orchestrationmicroservice 2342, the DPaaS microservice 2342, and so on. The edgedevice 2302 also includes one or more storage service agents 2322, 2324,2326 that perform an action based on the control messages. For example,the edge device 2302 may include a software update agent 2322 thatresponds to control messages issued by the control plane 2346 based on auser indication to deploy a software update. Thus, in some examples, theedge device 2302 is configured to receive an “over-the-air” (OTA)software update from the cloud-based storage service 2304 via the datacommunications link 2306. The edge device 2302 may also include astorage orchestration agent 2324 that responds to control messagesissued by the storage orchestration microservice 2342 such as, forexample, control messages to create, modify, or move volumes. The edgedevice 2302 may also include a DPaaS agent 2326 that responds to controlmessages issued by the DPaaS microservice 2344, such as, for example,control messages to set data protection policies, take snapshots, and soon. It will be recognized that the edge device 2302 may include avariety of other agents directed to storage and data services that areprovided in the cloud-based storage service 2304 and carried out on theedge device 2302. The EMS client 2320 routes control messages receivedfrom the cloud-based storage service 2304 to the appropriate storageservice agent 2322, 2324, 2326.

In some implementations, the storage service agents 2322, 2324, 2326interface with a storage operating environment 2310. In some examples,the storage operating environment 2310 is a storage and data servicesapplication that executes on a storage controller 2312; however, inother examples the storage operating environment 2310 may include a setof containerized applications executing on the storage controller 2312.In examples where the edge device 2302 is a storage system, the edgedevice 2302 includes the storage controller 2312 and storage resources2314. The storage controller 2312 and storage resources 2314 may be anyof the storage controllers and storage resources discussed above. Inexamples where the edge device 2302 is a separate server or appliance,the storage service agents 2322, 2324, 2326 interface with a storageoperating environment of a storage controller on a storage system thatis collocated with the edge device 2302.

In some implementations, the storage service agents 2322, 2324, 2326 areconfigured to invoke storage system APIs exposed by the storageoperating environment 2310, a storage system API server, or some otherstorage system component. For example, a storage controller may includea device API server 2316 that exposes a library of device APIs 2318 forthe storage system. When a control message encapsulating a storage agentAPI request is routed by the EMS client 2320 to one of the storageservice agents 2322, 2324, 2326, that storage service agent fulfills therequest by invoking one or more device APIs 2318 exposed by the deviceAPI server 2316. As previously discussed, each storage service agent2322, 2324, 2326 is tailored to perform a specific set of functionsoffered by a corresponding component of the cloud-based storage service2304. For example, the update agent 2322 is tailored to performingsoftware updates (e.g., updates to the storage operating environment)driven by the software update microservice 2340, the storageorchestration agent 2324 is tailored to performing storage provisioningoperations driven by the storage orchestration microservice 2342, theDPaaS agent 2326 is tailored to performing data protection operationsdriven by the DPaaS microservice 2344. Accordingly, each storage serviceagent 2322, 2324, 2326 is provided with a set of permissions to accessonly the subset of device APIs 2318 necessary for carrying out itstailored operations. For example, the storage orchestration agent 2324may have API permissions to provision a volume but not definereplication targets, whereas the DPaaS agent 2326 may have APIpermissions to configure replication but not provision a volume.Similarly, the update agent 2322 may have permissions to update thestorage operating environment 2310 but not to provision a volume.

In some implementations, each storage service agent 2322, 2324, 2326 isassociated with a respective manifest that defines a set of permissionclaims specifying device APIs or families of device APIs to which agentrequires access. These permission claims are directed only to the set ofAPIs necessary for carrying out the agent's operations, which is asubset of the set of device APIs exposed by the edge device 2302 or astorage system associated with the edge device 2302. In some examples,the manifest also defines actions that can be taken with respect to theAPIs for which access is requested. For example, the actions may includecreate, modify, delete, apply, and so on. In some examples, the manifestis included in an installation package for the agent. The manifestand/or installation package may be signed by an authority or include acertificate for the permission claims and actions enumerated by themanifest. For example, the installation package for the agent may besigned by the cloud-based storage service. In some examples, each agentis assigned one or more tokens indicative of access privileges thatallow the agent to access a subset of the device APIs 2318 that areneeded to respond to a control message. When invoking a device API, thestorage service agent presents the certificate or access token, forexample, to the device API server 2316. Thus, the storage service agents2322, 2324, 2326 are not privileged in that they are not applicationsthat run as root and do not have access to the file system; rather; thestorage service agents 2322, 2324, 2326 only interact with the storageoperating environment 2310 through authorized APIs 2318 that they areprivileged to use. This prevents an agent from potentially exposing abackdoor into the storage operating environment 2310. In some examples,the manifest may identify permission claims to a family of APIs. Theparticular device APIs that are included in a family may be platformdependent. For example, a “volume” family may include all APIs relatingto creating, modifying, or deleting volumes, whereas a “software” familymay include all APIs relating to installation and upgrade of software.Thus, when the agent is granted an access token for a family, the accesstoken applies to all device APIs that are included in that family.

In some implementations, the edge management service 2350 of thecloud-based storage service 2304 includes an EMS messaging service 2354for passing control messages from the control plane the edge device, andan EMS configuration service 2352 for verifying and publishing agentinstallation packages. In some examples, the EMS configuration service2352 exposes an API for submitting an agent installation package. Forexample, when a developer creates a new storage service agent, thedeveloper submits an installation package that includes the storageservice agent and the manifest for the storage service agent. Themanifest includes the permission claims, as discussed above, as well asmetadata such as the name, version, author of the agent and a hash ofthe image file embodying the agent. The manifest may also describe a setof behaviors of the agent. The developer may submit the agentinstallation package by invoking a particular API function of the EMSconfiguration service 2352. The EMS configuration service 2352 may alsoexpose an API for approving an agent installation package. For example,once an agent installation package has been received, an administratorof the cloud-based storage service 2304 may review the installationpackage. The administrator may approve the installation package byinvoking an API function of the EMS configuration service 2352 thatsigns the installation package. In some examples, the signedinstallation package is made available to the EMS client 2320 of an edgedevice 2302 through the edge management service 2350 or other aspect ofthe cloud-based storage service 2304. In some implementations, the EMSconfiguration service 2352 can an API function to deploy an agent to anedge device 2302, an API function to remove an agent to an edge device2302, an API function to upgrade an agent already deployed on an edgedevice 2302, as well as others. In some examples, messages passedbetween the EMS client 2320 and the edge management service 2350 arecryptographically signed in both directions.

In some implementations, the edge device 2302 includes a containerorchestrator 2330 that deploys containerized applications that embodythe storage service agents 2322, 2324, 2326. In some examples, the EMSclient 2320 provides an indication to the container orchestrator 2330 todownload an agent package and install a container image embodying thestorage service agent. For example, a user/administrator may direct thecloud-based storage service 2304, via the user interface, to deploy aparticular storage service agent on a particular edge device. In someexamples, the cloud-based storage service informs the user regarding thepermissions that will be granted to the storage service agent. Thecloud-based storage service 2304 may publish a message indicating to theEMS client 2320 that the particular storage service agent should bedeployed on the edge device 2302. The EMS client 2320 may then directthe container orchestrator 2330 to download the agent package, which hasbeen signed by the cloud-based storage service, and install the agent.In some examples, the agent package is downloaded from the cloud-basedstorage service 2304, while in other examples the agent package may bedownloaded from another trusted source.

In some implementations, the edge device 2302 also includes an IoTmessaging interface 2332. The IoT messaging interface 2332 supports anIoT message layer of the data communications link 2306 between the edgedevice 2302 and the cloud-based storage service 2304. The IoT messaginginterface 2332 sends and receives messages transmitted in accordancewith an IoT messaging protocol such as MQTT or other IoT messagingprotocols. For example, the IoT messaging interface 2332 may be an AWSGreengrass™ Core.

In some implementations, the edge device 2302 also includes a cloudconnect manager (CCM) 2334. The CCM 2334 performs certificate managementas well as log upload to the cloud-based storage service 2304. Forexample, the CCM 2334 authenticates communication received from thecloud-based storage service 2304.

In some implementations, the edge device 2302 also includes a proxy 2336for communication with the cloud-based storage service 2304 and forcommunication among components of the edge device such as the EMS client2320, the storage service agents 2322, 2324, 2326, the device API server2316, the container orchestrator 2330, the IoT messaging interface 2332,the CCM 2334, and other components that may not be discussed here. Insome examples, the proxy 2336 is an application layer (OSI layer 7)proxy for HTTP message routing. For example, the proxy 2336 may be anEnvoy proxy.

Without loss of generality, consider a non-limiting example where anenterprise administrator installs the EMS client 2320 on the edge device2302. Once installed, the EMS client 2320 registers the edge device 2302with the edge management service 2350. Subsequently, the enterpriseadministrator requests the edge management service 2350 to deploy astorage service agent to an enterprise edge device 2302. In thisexample, the administrator makes an RPC to the edge management service2350 of the cloud-based storage service 2304 specifying a storageservice agent to deploy and one or more edge devices on which thestorage service agent should be deployed. The edge management service2350 may employ step authentication to verify the identity of theadministrator before deploying the agent. The edge management service2350 publishes a message (e.g., an IoT message) that is read by the EMSclient 2320 of an edge device 2302. The EMS client 2320 directs thecontainer orchestrator 2330 to download the agent installation packagefor that storage service agent. The container orchestrator 2330 verifiesthat the installation package is signed by an authority of thecloud-based storage service 2304 and, if verified, installs the agent onthe edge device 2302. The EMS client 2320 ingests the manifest for thestorage service agent and provides the agent with a token forpermissions to APIs or API families and actions specified in themanifest. For example, where the storage service agent is a storageorchestration agent, the manifest may include a permission claim for a“volume” family of APIs and actions that include “create” and “delete.”In this example, the storage orchestration agent is assigned a tokengranting access to device APIs that allow the storage orchestrationagent to create or delete a volume.

Without loss of generality, consider another non-limiting example wherea storage orchestration microservice 2442 makes an RPC (e.g., a gRPC) tothe storage orchestration agent 2322 to create a new volume. A controlmessage including the RPC is published by the edge management service2350 and read by the EMS client 2320. For example, the control messagemay be published in an IoT messaging layer that is read through IoTmessaging interface 2332. The EMS client 2320 routes the RPC to thestorage orchestration agent 2322, which calls a device API to create anew volume. The device API server 2316 determines, based on a tokenpresented by the storage orchestration agent 2322, whether the storageorchestration agent 2322 is permitted to make the API call. If the agentis permitted, the device API for creating a new volume is invoked.

For further explanation, FIG. 24 sets forth a flow chart illustrating anexample method of employing an edge management service according to someembodiments of the present disclosure. The example method of FIG. 24includes configuring 2402, on an edge device 2401, a storage serviceagent 2405 to access a particular set 2411 of storage system applicationprogramming interfaces (APIs) 2421, 2423 of at least one enterprisestorage system, the storage service agent 2405 communicatively coupledto a cloud-based storage service 2403. Although depicted in less detail,the edge device 2401 and the cloud-based storage service 2403 depictedin FIG. 24 may be similar to the edge device 2302 and the cloud-basedstorage service 2404 described above with reference to FIG. 23. In fact,the edge device 2401 and the cloud-based storage service 2403 depictedin FIG. 24 may include the same, fewer, additional components as any ofthe edge devices and the cloud-based storage services described above.In some embodiments, the edge device 2401 is provided as a device in astorage system such as a storage controller. In such embodiments, theedge device 2401 may be similar to any of the storage systems discussedabove. In fact, the edge device 2401 may include the same, fewer,additional components as any of the storage systems described above.

The edge device 2401 includes one or more storage service agents 2405,2407 (e.g., the storage service agents 2322, 2324, 2326 of FIG. 23) thatare responsive to control messages generated by a cloud-based controlplane (e.g., the cloud-based control plane 2346 of FIG. 23) of thecloud-based storage service. For example, the cloud-based control planecan embody a storage service application, a set of storagemicroservices, a storage orchestrator, or combinations thereof. In oneexample, there is a one-to-one correspondence between a set of storagemicroservices of the cloud-based storage service 2403 and a set ofstorage service agents 2405, 2407 on the edge device 2401, such thateach storage service agent 2405, 2407 on the edge device 2401 isresponsive to control messages from a particular storage microservice ofthe cloud-based storage service 2403. In some implementations, storageservice agents 2405, 2407 are containerized applications. For example,the storage service agents 2405, 2407 can be containerized applicationsdeployed by the cloud-based storage service 2403. In some examples,where the edge device 2401 is an enterprise storage system, the storageservice agents 2405, 2407 may execute on a storage controller of theenterprise storage system.

The edge device 2401 and cloud-based storage service 2403 communicatevia data communications link 2409 through which the storage serviceagents 2405, 2407 are coupled to the cloud-based storage service 2403,and more particularly to the cloud-based control plane. In someexamples, the data communications link 2409 includes an IoT messaginglayer though implementation of an IoT messaging protocol. For example,the edge device 2401 may include an EMS client (e.g., the EMS client2320 of FIG. 23) that reads control messages published by the cloud-basestorage service 2403 over the IoT messaging layer. In such examples, theEMS client reads control messages directed to edge device 2401 anddirects those control messages to storage service agents 2405, 2407.

The storage service agents 2405, 2407 each have a limited operationalscope. For example, each storage service agent 2405, 2407 may correspondto a specific storage service such as storage-as-code, data protection,disaster recovery, update deployment, and so on. As described above, thecloud-based storage service 2403 may provide all of these services as amonolithic storage services application or as a set of microservicessuch as cloud-native containerized applications. In the case of thelatter, each storage service agent 2405, 2407 may correspond to arespective control plane microservice of the cloud-based storage service2403. In this case, to carry out operations on the storage system, eachcloud-based microservice calls on its corresponding storage serviceagent 2405, 2407, for example, through a remote procedure call such asgRPC. In other cases, the cloud-based storage service 2403 may selectone of the storage service agents 2405, 2407 based on the nature of theremote procedure call.

The storage service agents 2405, 2407 interact with storage system APIs2421, 2423, 2425, 2427 to carry out operations on the storage systemthat are directed by the control plane of the cloud-based storageservice 2403. Examples of storage system APIs can include APIs to createa new volume, migrate a volume, configure a host connection, set asnapshotting frequency, define a replication target, and so on. Readerswill appreciate that a variety of APIs are provided by a storage system.The set of APIs exposed by a storage system may be referred to here as alibrary of APIs. As discussed above, in some implementations the edgedevice 2401 may be a component of enterprise storage system. In suchimplementations, as depicted in FIG. 24, the library of storage systemAPIs is provided on the edge device 2401 itself. In otherimplementations, the edge device 2401 may be a collocated server coupledto the enterprise storage system via a network connection. In additionalimplementations, the edge device 2401 may be a virtual device. In someexamples, the storage system APIs 2421, 2423, 2425, 2427 are accessed bythe storage service agents 2405, 2407 through an API server.

To deter the cloud-based storage service 2403 and the storage serviceagents 2405, 2407 from being used maliciously to gain access to theenterprise storage system, the storage service agents 2405, 2407 arelimited to accessing only those storage system APIs that are necessaryto carry out the operations of that storage service agent. Forillustration in FIG. 24, a particular service agent 2405 can only accessa particular set 2411 of storage system APIs 2421, 2423 from among thelibrary 2413 of storage system APIs 2421, 2423, 2425, 2427. To furtherdeter malicious external services to impersonate the cloud, and therebyinteract with the agent on the device (and carry outunauthenticated/unauthorized operations), all messages (e.g., datapackets) transferred between the cloud service and the agent on the edgedevice are encrypted and signed by mutually known private/public keys orother credential systems/ciphers appropriate for signing metadata andencryption.

In some examples, the edge device 2401 configures 2402 the storageservice agent 2405 to access the particular set 2411 of storage systemAPIs 2421, 2423 by identifying various storage system APIs that fallwithin the operational scope of the storage service agent 2405. Forexample, a storage orchestration agent may require access to APIs tocreate a volume but not to set disaster recovery policies. In someexamples, the storage system APIs that are applicable to the operationalscope of the storage service agent 2405 are specified in a document orfile associated with the storage service agent 2405. For example, thedocument may be a certificate, an install package manifest, or othertrusted document. In such examples, the document or file may be signedby an authority associated with the cloud-based storage service 2403 orsome other authority.

In some examples, the edge device 2401 configures 2402 the storageservice agent 2405 to access the particular set 2411 of storage systemAPIs 2421, 2423 by also granting a credential for the particular set2411 of storage system APIs 2421, 2423 to the storage service agent2405. In some examples, granting a credential for the particular set2411 of storage system APIs 2421, 2423 can include providing acertificate, access token, or other credential to the storage serviceagent 2405 either for the entire set 2411 of storage system APIs 2421,2423 or for each storage system API 2421, 2423 individually. Thus, thestorage service agent 2405 cannot access other storage system APIs 2425,2427 that are not included in the particular set 2411 of storage systemsAPIs 2421, 2423 for which access has been configured.

The method of FIG. 24 also includes invoking 2404, by the storageservice agent 2405 in response to a control message 2415 from thecloud-based storage service 2403, one or more storage system APIs of theparticular set 2411 of storage system APIs 2421, 2423. In some examples,the edge device 2401 receives a control message 2415 from the controlplane of the cloud-based storage service 2403 over the datacommunications link 2409. The control message 2415 message is routed tothe appropriate storage service agent 2405 by a message router such as,for example, the EMS client. In some implementations, the controlmessage 2415 includes a remote procedure call (e.g., a gRPC). Forexample, a storage orchestration microservice of the cloud-based storageservice 2403 may publish an RPC to the edge device 2401, where the RPCis a call to create a new volume. In this example, the EMS clientforwards the RPC to the storage orchestration agent. In someimplementations, the control message 2415 may be embodied in an IoTmessage that is received by the edge device 2401 through the IoTmessaging layer. For example, the EMS client may receive the controlmessage 2415 via an IoT messaging interface (e.g., the IoT messaginginterface 2332 of FIG. 23). In response to receiving the control message2415, the storage service agent 2405 invokes a storage system API 2421from among the set 2411 of storage system APIs 2421, 2423 for whichaccess has been configured. Continuing the above example, when thestorage orchestration agent receives the RPC to create a new volume, thestorage orchestration agent invokes a storage system API to create thenew volume on the storage system. In some examples, all controlmessages, such as RPCs, issued by the services in the cloud-basedstorage service 2403 and directed to the storage service agents 2405,2407 are relayed between an EMS messaging service of the cloud-basedstorage service 2403 and an EMS client of the edge device 2401, therebyproviding a standard messaging format and control channel pathway forall such control messages. For example, the EMS messaging service of thecloud-based storage service 2403 and an EMS client of the edge device2401 may be couple via the IoT messaging layer.

For further explanation, FIG. 25 sets forth a flow chart illustrating anexample method of employing an edge management service according to someembodiments of the present disclosure. The example method of FIG. 25includes aspects of the method of FIG. 24. In the example method of FIG.25, configuring 2402 the storage service agent 2405 to access theparticular set 2411 of storage system APIs 2421, 2423 includesidentifying 2502 one or more storage system API permission claimsassociated with the storage service agent 2405. As used here, an APIpermission claim is a declaration by a developer that the storageservice agent 2405 requires access to a particular storage system API orfamily of storage system APIs to carry out ordained operations that arecommensurate with the scope of the storage service agent 2405. Forexample, a storage orchestration agent may require access to an API tocreate storage volumes, or access to a family of APIs related to storagevolumes (e.g., creating, modifying, migrating, etc.). In some examples,the API permission claim may be associated with one or more actionswithin a family of APIs. For example, a permission claim to storagevolume family of APIs may be directed to actions for creating a storagevolume but not migrating the storage volume. In some examples, the APIpermission claims for the storage service agent 2405 are specified in adocument or file associated with the storage service agent 2405. Forexample, the document may be a certificate, an install package manifest,or other authorized document. In such examples, the document or file maybe signed by an authority associated with the cloud-based storageservice 2403 or some other authority.

In some examples, the edge device 2401 configures 2402 the storageservice agent 2405 to access the particular set 2411 of storage systemAPIs 2421, 2423 by also determining the particular set 2411 of storagesystem APIs 2421, 2423 that correspond to the permission claims. In someexamples, the edge device 2401 determines which storage system APIs2421, 2423 among the library 2413 of storage system APIs correspond tothe permission claims. For example, the particular set 2411 of storagesystem APIs 2421, 2423 may be determined based on API libraryarchitecture or structure indicating relatedness among APIs, from APInaming conventions, by predetermined mappings of permission claims toAPI sets, or through explicit enumeration of individual APIs in thepermission claims.

In the example of FIG. 25, configuring 2402 the storage service agent2405 to access the particular set 2411 of storage system APIs 2421, 2423also include includes assigning 2504, to the storage service agent 2405in dependence upon the one or more storage system API permission claims,one or more storage system API access tokens corresponding to theparticular set 2411 of storage system APIs 2421, 2423. In some examples,the edge device 2401 assigns access token to the storage service agent2405 either for the set 2411 of storage system APIs 2421, 2423 or foreach storage system API 2421, 2423 individually. This access token mustbe provided when invoking one of the storage systems APIs 2421, 2423,and no storage system APIs in the library 2413 of storage system APIs2421, 2423, 2425, 2427 can be successfully invoked without providing atoken to do so. Thus, the storage service agent 2405 cannot access otherstorage system APIs 2425, 2457 that are not included in the particularset 2411 of storage systems APIs 2421, 2423 for which access has beenconfigured.

For further explanation, FIG. 26 sets forth a flow chart illustrating anexample method of employing an edge management service according to someembodiments of the present disclosure. The example method of FIG. 26includes aspects of the method of FIG. 24. In the example method of FIG.25, invoking 2404, by the storage service agent 2405 in response to acontrol message 2415 from the cloud-based storage service 2403, one ormore storage system APIs of the particular set 2411 of storage systemAPIs 2421, 2423 includes determining 2602 whether the storage serviceagent is permitted to access the one or more storage system APIs. Insome examples, the edge device 2401 determines whether the storageservice agent 2405 has the credentials to access the storage system APIthat is required to carry out an RPC. In some implementations, an EMSclient, upon reading the control message, determines whether the storageservice agent 2405 is configured for access to the storage system API.For example, the EMS client may determine whether the storage serviceagent 2405 has an access token for the storage system API. If thestorage service agent 2405 has an access token for the storage systemAPI, the EMS client forwards the RPC to the storage service agent. Inother implementations, a storage system API server determines whetherthe storage service agent 2405 is configured for access to the storagesystem API. For example, the storage system API server may determinewhether the storage service agent 2405 has an access token for thestorage system API that is called. If the storage service agent 2405 hasan access token for the storage system API, the storage system APIserver permits invocation of the storage system API. Readers willappreciate that other components of the edge device 2401 may be taskedwith verifying whether a storage service agent 2405 is permitted to makean storage system API call.

For further explanation, FIG. 27 sets forth a flow chart illustrating anexample method of employing an edge management service according to someembodiments of the present disclosure. The example method of FIG. 27includes aspects of the method of FIG. 24. The method of FIG. 27 alsoincludes identifying 2702, from the cloud-based storage service 2403, anindication to install the storage service agent 2405 on the edge device2401. In some examples, an indication to install the storage serviceagent 2405 may be made in response to request from the administrator ofan enterprise storage system. For example, the administrator mayrequest, through a management plane API or user interface, that thestorage service agent 2405 be deployed to the edge device 2401. Inresponse, the cloud-based storage service 2403 may set a stateindicating that the storage service agent 2405 is to be installed on theedge device 2401 and/or send a message to the edge device indicating thesame. Upon observing the state or reading the message, the edge device2401 identifies the storage service agent 2405 for installation on theedge device 2401. In some implementations, an EMS client of the edgedevice 2401 is configured to determine, based on information provided bythe cloud-based storage service 2403, that the storage service agent2405 is to be installed on the edge device 2401.

The example method of FIG. 27 also includes obtaining 2704, in responseto identifying the indication to install the storage service agent 2405,an agent install package for the storage service agent 2405. In someexamples, the edge device 2401 obtains the agent install package bydownloading the agent install package through the cloud-based storageservice. For example, an EMS client, upon identifying the indication toinstall the storage service agent 2405, may direct a containerorchestrator to download the install package through the cloud-basedstorage service 2403. In some variants, the install package may bedownloaded from another trusted package provider instead of thecloud-based storage service. In some implementations, the cloud-basedstorage service 2403 pushes the install package to the edge device 2401without a request for the install package from the edge device 2401. Theinstall package may be deployed to the edge device 2401 through the IoTmessaging layer as an over-the-air software package.

In some examples, the install package for the storage service agentincludes an image file for the storage service agent and a manifest. Forexample, the manifest may include metadata such as an agent name,version, author/developer, organization, and a hash of the image file.The manifest includes permission claims for the storage service agent,as discussed above. In some examples, a permission claim may be anindication of one or more storage system APIs that are utilized by thestorage service agent. In some examples, a permission claim may be anindication of one or more families of storage system APIs that areutilized by the storage service agent. The families of storage systemAPIs may be groupings of storage system APIs related to a particularaspect of the storage system. For example, one family of storage systemAPIs may be directed to APIs for volume creation, configuration andmanagement, while another family of storage APIs may be directed to hostconfiguration and management, and so on. In some implementations, themanifest also includes one or more types of actions associated with eachpermission claim. A permission claim does not necessarily apply to alltypes of actions permissible with the API family. For example, withrespect to a particular storage system API or family of APIs, themanifest may identify types of actions such a “create” or “modify” for apermission claim, but not other types of actions such as “delete” or“destroy.” Thus, as one example, a permission claim for a “volumes”family of storage system APIs that is associated with the “create” and“modify” actions defines a request for access to storage system APIsthat will allow the agent to create and modify, but not delete ordestroy, volumes on the storage system. In some implementations, themanifest also identifies behaviors of the storage service agent 2405such as runtime behaviors and high-availability behaviors. For example,behaviors of the storage service agent 2405 may indicate whether astorage service agent remains active 2405 on a passive secondarycontroller of the storage system. As one example, an update agent may beactive on all storage controllers, whereas a storage orchestration agentmay be active only on the active storage controller. In some examples,the install package is signed by the cloud-based storage service 2403 toensure that the storage service agent and manifest have been verifiedthe cloud-based storage service 2403.

The example method of FIG. 27 also includes installing 2706, from theagent install package, the storage service agent 2405 on the edge device2401. Before installing the storage service agent, the edge device 2401verifies the digital signature of the cloud-based storage service 2403that is included in the install package for the storage service agent.In some examples, a container orchestrator on the edge device 2401installs the image file for the storage service agent from the installpackage.

For further explanation, FIG. 28 sets forth a flow chart illustrating anexample method of employing an edge management service according to someembodiments of the present disclosure. The example method of FIG. 28 isdirected to aspects of a cloud-based storage service 2803, such as anyof the cloud-based storage services described above. In some examples,the cloud-based storage service 2803 includes a control plane for themanagement of a fleet of enterprise storage systems. In someimplementations, the control plane also provides data services for thefleet of enterprise storage systems. In one example, the control planeis a monolithic storage and data services application, which may beembodied in a cloud-compute instance. In other examples, the controlplane includes a collection of storage and data microservices. Forexample, these microservices may be containerized storage and dataservices applications deployed in the cloud. In such examples, there maybe a storage service agent 2805 for each storage microservice of thecloud-based storage service 2803, where the storage service agent isinstalled on one or more edge devices 2801. In this way, the storageservice agent represents a local extension of the correspondingmicroservice of the cloud-based storage service 2803. In some examples,an edge device 2801 may itself be an enterprise storage system. In someexamples, the cloud-based storage service may include an edge managementservice. In such examples, the edge management service may manage thedeployment and update of the storage service agents on the edge devices2801, as well as the messaging between the control plane of thecloud-based storage service 2803 and the storage service agents 2805 onthe edge devices.

The example method of FIG. 28 includes receiving 2802, by a cloud-basedstorage service 2803, an installation package for a storage serviceagent 2805. In some examples, as mentioned above, an author or developerof a storage service agent (or software update to a storage serviceagent) provides a storage service agent package to the cloud-basedstorage service 2803. The storage service agent package includes, forexample, an image file or location of an image file (e.g., a containerregistry) of the storage service agent 2805 and a manifest for thestorage service agent 2805. A manifest, such as those discussed above,for the storage service agent 2805 describes a limited set of storagesystem APIs accessed by the storage service agent 2805 to carry out theoperations of the storage service agent 2805. For example, the manifestmay include permission claims and other information as discussed above.A developer may indicate which storage system APIs or API families arecalled by the storage system agent. Thus, the manifest represents anenumeration of essential storage system APIs. Accordingly, using themanifest, an edge device 2801 may restrict agent access to non-essentialstorage system APIs. The manifest may also include other informationdescribing runtime behaviors or information specific to particularplatforms. The manifest may also include metadata such as an agent name,version, author/developer/team, organization, a description of theagent, and a hash of the image file. In some implementations, thestorage service agent package is submitted to the cloud-based storageservice 2803 via an API exposed by an edge management service of thecloud-based storage service 2803. In some implementations, the APIincludes a procedure call (e.g., gRPC) to parse structure data such aJSON or YAML file that embodies the manifest and is provided by thedeveloper. In other implementations, the manifest may be submitted as astandalone JSON or YAML file. Readers will appreciate that a manifestfor a storage service agent package may be provided in a variety ofother formats.

The method of FIG. 28 also includes signing 2804, by the cloud-basedstorage service 2803, the installation package for the storage serviceagent 2805. In some examples, an API call to submit a storage serviceagent triggers a notification or other indication to administrators orother personnel associated with the cloud-based storage service 2803.For example, a package review team may review the storage agent packageand confirm that the permission claims and action types enumerated inthe manifest do not exceed the scope of the storage service agent. Inother words, a package review team may identify whether the manifestincludes a permission claim to an API or API family that is notessential to the functionality to the storage service agent. In someimplementations, the edge management service of the cloud-based storageservice 2803 exposes an API to approve the agent package. In theseimplementations, a member of the package review team may invoke this APIto approve the agent package. In response to approval of the package,the cloud-based storage service 2803 signs the install package with itsdigital signature.

The method of FIG. 28 also includes deploying 2806, by the cloud-basedstorage service 2803, the installation package for the storage serviceagent 2805 to one or more edge devices 2801, 2807. In some examples, astorage service agent 2405 is deployed to one or more edge devices 2801in response to request from the administrator of an enterprise storagesystem. For example, the administrator may request, through a managementplane API or user interface of an edge management service, that thestorage service agent 2805 be deployed to the edge device 2801. Therequest may indicate the storage service agent to deploy and which edgedevices 2801 on which the storage service agent should be deployed. Inresponse, the edge management service of the cloud-based storage service2403 may deploy 2806 the storage service agent 2805 by setting a stateindicating that the storage service agent 2805 is to be installed on anedge device 2801 and/or sending a message to the edge device 2801indicating the same. In some implementations, deploying 2806 the storageservice agent may include pushing the storage service agent 2805 (i.e.,a storage service agent install package) to an edge device 2801. Thestorage service agent 2805 may be deployed or updated on a per-edgedevice basis, in that an administrator may wish to deploy the storageservice agent on some but not all of the edge devices 2801 for a fleetof enterprise storage systems. Further, the storage service agents maybe deployed and updated independently, such that portions of the controlplane may be inaccessible during update of the storage service agentwithout suspending all functionality of the control plane, and withoutany effect on the data plane. In other words, the edge managementservice of the cloud-based storage service 2803 may deploy a pluralityof storage service agents 2805 to a set of edge devices 2801, and thenupdate a single storage service agent without updating the remainingagents on the set of edge devices, or even just update a single storageservice agent on a single edge device.

For further explanation, FIG. 29 sets forth a flow chart illustrating anexample method of employing an edge management service according to someembodiments of the present disclosure. The example method of FIG. 29includes aspects of the example method of FIG. 28. However, the examplemethod of FIG. 29 also includes generating 2902 a control messagedirected to a storage service agent on at least one of the edge devices,wherein the storage service agent interfaces with one or more storagesystem application programming interfaces (APIs). In some examples, acontrol plane of the cloud-based storage service generates a controlmessage directed to a storge service agent 2805 on the edge device 2801.For example, the control message can be an RPC (e.g., a gRPC) to astorage service agent that in turn invokes a storage system API inresponse to the RPC. In some implementations, the control message isprovided by the control plane to an EMS messaging service in thecloud-based storage service 2803. In these implementations, the EMSmessaging service may provide the control message to an EMS client ofthe edge device 2801. In some examples, the control message may beembodied in an IoT message that is received by the edge device 2801through the IoT messaging layer. As one example, a storage orchestrationmicroservice of the cloud-based storage service 2803 may publish an RPCdirected to a storage orchestration agent on the edge device 2801, wherethe RPC is a call to create a new volume.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Readers will appreciate that the steps described herein may be carriedout in a variety ways and that no particular ordering is required. Itwill be further understood from the foregoing description thatmodifications and changes may be made in various embodiments of thepresent disclosure without departing from its true spirit. Thedescriptions in this specification are for purposes of illustration onlyand are not to be construed in a limiting sense. The scope of thepresent disclosure is limited only by the language of the followingclaims.

What is claimed is:
 1. A method comprising: configuring, on an edgedevice, a storage service agent to access a particular set of storagesystem application programming interfaces (APIs) of at least oneenterprise storage system, the storage service agent communicativelycoupled to a cloud-based storage service; and invoking, by the storageservice agent in response to a control message from the cloud-basedstorage service, one or more storage system APIs of the particular setof storage system APIs.
 2. The method of claim 1, wherein the edgedevice is one of a component of the enterprise storage system, a servercollocated with the enterprise storage system, and a virtual device. 3.The method of claim 1, wherein the cloud-based storage service includesat least one of a cloud-based control plane, a cloud-based storageorchestrator, and a set of storage microservices.
 4. The method of claim1, wherein the storage service agent is a containerized applicationdeployed by the cloud-based storage service.
 5. The method of claim 1,wherein the particular set of storage system APIs is a subset of astorage system API library; and wherein the access is restricted to theparticular set of storage system APIs.
 6. The method of claim 1, whereina data communications link between the storage service agent and thecloud-based storage system includes an Internet-of-Things (IoT)messaging layer.
 7. The method of claim 6, wherein a messaging serviceof the cloud-based storage service is coupled to a messaging client ofthe edge device via the IoT messaging layer; wherein a plurality ofmicroservices the cloud-based storage service utilize the messagingservice for sending control messages to a plurality of storage serviceagents on the edge device.
 8. The method of claim 1, whereinconfiguring, on an edge device, a storage service agent to access aparticular set of storage system APIs of at least one enterprise storagesystem includes: identifying one or more storage system API permissionclaims associated with the storage service agent; and assigning, to thestorage service agent in dependence upon the one or more permissionclaims, one or more storage system API access tokens corresponding tothe particular set of storage system APIs.
 9. The method of claim 1,wherein invoking, by the storage service agent in response to a controlmessage from the cloud-based storage service, one or more storage systemAPIs of the particular set of storage system APIs includes: determiningwhether the storage service agent is permitted to access the one or morestorage system APIs.
 10. The method of claim 1 further comprising:identifying, from the cloud-based storage service, an indication toinstall the storage service agent on the edge device; obtaining, inresponse to identifying the indication to install the storage serviceagent, an agent install package for the storage service agent;installing, from the agent install package, the storage service agent onthe edge device.
 11. The method of claim 10, wherein the agent installpackage include a manifest that describes storage system API permissionclaims for the storage service agent.
 12. The method of claim 10,wherein a permission claim identifies a storage system API family andone or more types of actions for the identified storage system APIfamily.
 13. The method of claim 10, wherein the agent install package issigned by an authority associated with the cloud-based storage service.14. A method comprising: receiving, by a cloud-based storage service, aninstallation package for a storage service agent; signing, by thecloud-based storage service, the installation package for the storageservice agent; and deploying, by the cloud-based storage service, theinstallation package for the storage service agent to one or more edgedevices.
 15. The method of claim 14, wherein the storage service agentis a local extension of a microservice provided by the cloud-basedstorage service.
 16. The method of claim 14, wherein the installationpackage includes a manifest for the storage service agent; and whereinthe manifest identifies one or more storage permission claims for one ormore storage system application programming interfaces (APIs).
 17. Themethod of claim 14, wherein the installation package for the storageservice agent is signed in response to an indication that theinstallation package has been verified by a human user.
 18. The methodof claim 14, wherein the cloud-based storage service deploys a pluralityof storage service agents to each of the one or more edge devices; andwherein a software update to a single storage service agent is deployedto the one or more edge devices.
 19. The method of claim 14 furthercomprising: generating a control message directed to the storage serviceagent on at least one of the edge devices, wherein the storage serviceagent interfaces with one or more storage system application programminginterfaces.
 20. An apparatus comprising a computer processor, a computermemory operatively coupled to the computer processor, the computermemory having disposed within it computer program instructions that,when executed by the computer processor, cause the apparatus to:configure, on an edge device, a storage service agent to access aparticular set of storage system application programming interfaces(APIs) of at least one enterprise storage system, the storage serviceagent communicatively coupled to a cloud-based storage service; andinvoke, by the storage service agent in response to a control messagefrom the cloud-based storage service, one or more storage system APIs ofthe particular set of storage system APIs.